7002_011-Niov_25_TimeSeriesII.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VicDc/Uruz01/blob/main/7002_011-Niov_25_TimeSeriesII.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1WLrr5xUFWf"
      },
      "source": [
        "OPIT\n",
        "\n",
        "Instructor: Sabyasachi Dasgupta"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind"
      ],
      "metadata": {
        "id": "XPJ_4PuimDEb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class1_scores = [80, 80, 87, 65, 67, 79, 74, 88, 75, 70]\n",
        "class2_scores = [80, 80, 87, 65, 67, 79, 74, 88, 75, 70]"
      ],
      "metadata": {
        "id": "_ABK-f5Nm3bx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t_statistic, p_value = ttest_ind(class1_scores, class2_scores)"
      ],
      "metadata": {
        "id": "zvJy4Liim3UO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Output the results\n",
        "print(f\"t-statistic: {t_statistic}\")\n",
        "print(f\"P-value: {p_value}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvGtE8Bum3Lp",
        "outputId": "14e98c0c-c529-4cf8-c895-866b876e883d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "t-statistic: 0.0\n",
            "P-value: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if the result is statistically significant (using a common significance level of 0.05)\n",
        "if p_value < 0.05:\n",
        "    print(\"There is a significant difference between the exam scores of the two classes.\")\n",
        "else:\n",
        "    print(\"There is no significant difference between the exam scores of the two classes.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9r6qpXWnKTc",
        "outputId": "d881c27d-ee24-44b4-9c49-ad82f518745b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There is no significant difference between the exam scores of the two classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n"
      ],
      "metadata": {
        "id": "T6fAzP58n6Sd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"https://raw.githubusercontent.com/nicknochnack/TimeSeriesForecastingProphet/main/dataset.csv\")"
      ],
      "metadata": {
        "id": "sfZ86kG3n3Qp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "RRe90j11n9II",
        "outputId": "ca721ad8-2864-4cb1-c97b-a64afc663aa8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Time Date  Product          Store        Value\n",
              "0       1012018  2667437  QLD_CW_ST0203  2926.000000\n",
              "1       2012018  2667437  QLD_CW_ST0203  2687.531000\n",
              "2       3012018  2667437  QLD_CW_ST0203  2793.000000\n",
              "3       4012018  2667437  QLD_CW_ST0203  2394.000000\n",
              "4       5012018  2667437  QLD_CW_ST0203  2660.000000\n",
              "...         ...      ...            ...          ...\n",
              "1075   12122020  2667437  QLD_CW_ST0203  3167.594500\n",
              "1076   13122020  2667437  QLD_CW_ST0203  3167.594500\n",
              "1077   14122020  2667437  QLD_CW_ST0203  3383.354511\n",
              "1078   15122020  2667437  QLD_CW_ST0203  2714.591329\n",
              "1079   16122020  2667437  QLD_CW_ST0203  2585.035107\n",
              "\n",
              "[1080 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b13e52e3-b075-45f0-9fa4-ad0ed48a9dc4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time Date</th>\n",
              "      <th>Product</th>\n",
              "      <th>Store</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2926.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2687.531000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2394.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2660.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1075</th>\n",
              "      <td>12122020</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>3167.594500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1076</th>\n",
              "      <td>13122020</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>3167.594500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1077</th>\n",
              "      <td>14122020</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>3383.354511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1078</th>\n",
              "      <td>15122020</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2714.591329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1079</th>\n",
              "      <td>16122020</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2585.035107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1080 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b13e52e3-b075-45f0-9fa4-ad0ed48a9dc4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b13e52e3-b075-45f0-9fa4-ad0ed48a9dc4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b13e52e3-b075-45f0-9fa4-ad0ed48a9dc4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f77c437f-f67b-4b82-b184-fd81591321f4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f77c437f-f67b-4b82-b184-fd81591321f4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f77c437f-f67b-4b82-b184-fd81591321f4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_770faef0-81fb-47e4-b443-be52da90ccec\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_770faef0-81fb-47e4-b443-be52da90ccec button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 1080,\n  \"fields\": [\n    {\n      \"column\": \"Time Date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8791548,\n        \"min\": 1012018,\n        \"max\": 31122019,\n        \"num_unique_values\": 1080,\n        \"samples\": [\n          28022019,\n          3072019,\n          2012020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2667437,\n        \"max\": 2667437,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2667437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"QLD_CW_ST0203\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1439.945782927857,\n        \"min\": 2042.8135,\n        \"max\": 8147.7396,\n        \"num_unique_values\": 679,\n        \"samples\": [\n          2740.0128\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n"
      ],
      "metadata": {
        "id": "Cz3W3RS-nKKn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = adfuller(data['Value'])\n",
        "print('ADF Statistic:', result[0])\n",
        "print('p-value:', result[1])\n",
        "#If the p-value is greater than 0.05, the series is not stationary."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GYYSVvXWn1BR",
        "outputId": "bffa6e91-8409-48ad-e583-e95128ae50ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistic: -2.212478952793599\n",
            "p-value: 0.20174753235777343\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xD-9voz9UFWi"
      },
      "source": [
        "**What will we learn today?**\n",
        "\n",
        "* Facebook Prophet\n",
        "\n",
        "* Time Series as a Supervised Learning problem\n",
        "\n",
        "* Deep Learning for Stock Prediction\n",
        "\n",
        "* Time Series Final Project Brainstorm - exercise"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "311AjLCIge7T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2cFtdXIcUFWi"
      },
      "source": [
        "## Facebook Prophet\n",
        "\n",
        "* FB Prophet is a procedure for forecasting time series data using a powerful toolkit created by Facebook. It allows us to decompose and model trends from time series data.\n",
        "\n",
        "* It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvYTCYuLUFWj"
      },
      "source": [
        "### Why use Facebook Prophet?\n",
        "\n",
        "* It is accurate and fast\n",
        "    * It is used in many applications across Facebook and it produces reliable forecasts for planning and goal setting.\n",
        "    * Forescasts in a few seconds.\n",
        "* Fully automatic\n",
        "    * No manual effort. Robust to outliers, missing data, changes in time series.\n",
        "* Tunable forecasts\n",
        "    * Users can tweak and adjust forecasts. Add your domain knowledge!\n",
        "* R or Python\n",
        "*Allows us to solve various Business Analytics Questions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWJrNo1iUFWm"
      },
      "source": [
        "### Predicting and Decomposing Online Sales for a Company using FB Prophet!"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import requests, json, os, warnings, re\n",
        "\n",
        "# ignore warnings\n",
        "import warnings\n",
        "warnings.simplefilter(\"ignore\", UserWarning)"
      ],
      "metadata": {
        "id": "oihOYVW34CnZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip list"
      ],
      "metadata": {
        "id": "8jNpMvrTGjx-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Initialize\n",
        "\n",
        "# !pip install prophet # if you havn't done so\n",
        "import prophet\n",
        "from prophet import Prophet"
      ],
      "metadata": {
        "id": "gewOJGQQ4FuE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/nicknochnack/TimeSeriesForecastingProphet/main/dataset.csv\")"
      ],
      "metadata": {
        "id": "76dbaH5f4Sal"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(35)\n",
        "#df.columns\n",
        "#df.shape\n",
        "#df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4WTLkVzX4XaC",
        "outputId": "7cf306c9-c648-4301-9800-2f9dbe18bddb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Time Date  Product          Store      Value\n",
              "0     1012018  2667437  QLD_CW_ST0203  2926.0000\n",
              "1     2012018  2667437  QLD_CW_ST0203  2687.5310\n",
              "2     3012018  2667437  QLD_CW_ST0203  2793.0000\n",
              "3     4012018  2667437  QLD_CW_ST0203  2394.0000\n",
              "4     5012018  2667437  QLD_CW_ST0203  2660.0000\n",
              "5     6012018  2667437  QLD_CW_ST0203  2527.0000\n",
              "6     7012018  2667437  QLD_CW_ST0203  2527.0000\n",
              "7     8012018  2667437  QLD_CW_ST0203  2793.0000\n",
              "8     9012018  2667437  QLD_CW_ST0203  2793.0000\n",
              "9    10012018  2667437  QLD_CW_ST0203  2793.0000\n",
              "10   11012018  2667437  QLD_CW_ST0203  2926.0000\n",
              "11   12012018  2667437  QLD_CW_ST0203  2793.0000\n",
              "12   13012018  2667437  QLD_CW_ST0203  2869.8208\n",
              "13   14012018  2667437  QLD_CW_ST0203  3325.0000\n",
              "14   15012018  2667437  QLD_CW_ST0203  2261.0000\n",
              "15   16012018  2667437  QLD_CW_ST0203  2261.0000\n",
              "16   17012018  2667437  QLD_CW_ST0203  2253.6850\n",
              "17   18012018  2667437  QLD_CW_ST0203  2394.0000\n",
              "18   19012018  2667437  QLD_CW_ST0203  2394.0000\n",
              "19   20012018  2667437  QLD_CW_ST0203  2394.0000\n",
              "20   21012018  2667437  QLD_CW_ST0203  2527.0000\n",
              "21   22012018  2667437  QLD_CW_ST0203  2793.0000\n",
              "22   23012018  2667437  QLD_CW_ST0203  2660.0000\n",
              "23   24012018  2667437  QLD_CW_ST0203  2547.1362\n",
              "24   25012018  2667437  QLD_CW_ST0203  2926.0000\n",
              "25   26012018  2667437  QLD_CW_ST0203  2926.0000\n",
              "26   27012018  2667437  QLD_CW_ST0203  3059.0000\n",
              "27   28012018  2667437  QLD_CW_ST0203  2853.1027\n",
              "28   29012018  2667437  QLD_CW_ST0203  2793.0000\n",
              "29   30012018  2667437  QLD_CW_ST0203  2793.0000\n",
              "30   31012018  2667437  QLD_CW_ST0203  2816.4745\n",
              "31    1022018  2667437  QLD_CW_ST0203  2231.1548\n",
              "32    2022018  2667437  QLD_CW_ST0203  5212.0040\n",
              "33    3022018  2667437  QLD_CW_ST0203  5032.8530\n",
              "34    4022018  2667437  QLD_CW_ST0203  2253.6850"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3768a5c-73bc-4347-a1df-4ec16106ff25\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time Date</th>\n",
              "      <th>Product</th>\n",
              "      <th>Store</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2926.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2687.5310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2394.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2660.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2527.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2527.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2926.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2869.8208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>3325.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2261.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2261.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2253.6850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2394.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2394.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2394.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2527.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2660.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2547.1362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2926.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2926.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>3059.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2853.1027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>30012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>31012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2816.4745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>1022018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2231.1548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>2022018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>5212.0040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>3022018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>5032.8530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>4022018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2253.6850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3768a5c-73bc-4347-a1df-4ec16106ff25')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b3768a5c-73bc-4347-a1df-4ec16106ff25 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b3768a5c-73bc-4347-a1df-4ec16106ff25');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0603c9b1-2771-4596-a9ba-58abf88222a6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0603c9b1-2771-4596-a9ba-58abf88222a6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0603c9b1-2771-4596-a9ba-58abf88222a6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"#df\",\n  \"rows\": 35,\n  \"fields\": [\n    {\n      \"column\": \"Time Date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9594314,\n        \"min\": 1012018,\n        \"max\": 31012018,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          27012018,\n          14012018,\n          25012018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2667437,\n        \"max\": 2667437,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2667437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"QLD_CW_ST0203\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 635.175214979445,\n        \"min\": 2231.1548,\n        \"max\": 5212.004,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          2926.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "df['time_str'] = df['Time Date'].astype('str')\n",
        "print(datetime.strptime(df['time_str'][10],'%d%m%Y'),\n",
        "datetime.strptime(df['time_str'][10],'%d%m%Y').day,\n",
        "datetime.strptime(df['time_str'][10],'%d%m%Y').month,\n",
        "datetime.strptime(df['time_str'][10],'%d%m%Y').year)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tOuWggi24yb3",
        "outputId": "d936cc63-9726-4016-ba21-29956862527f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2018-01-11 00:00:00 11 1 2018\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YwBvdJ_Y9vwS",
        "outputId": "9efd4e86-9944-46f6-f7e6-9647e3cd5e58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time Date  Product          Store     Value time_str\n",
              "0    1012018  2667437  QLD_CW_ST0203  2926.000  1012018\n",
              "1    2012018  2667437  QLD_CW_ST0203  2687.531  2012018\n",
              "2    3012018  2667437  QLD_CW_ST0203  2793.000  3012018\n",
              "3    4012018  2667437  QLD_CW_ST0203  2394.000  4012018\n",
              "4    5012018  2667437  QLD_CW_ST0203  2660.000  5012018"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-97fee7a3-0cc3-481a-858d-44e606867ad8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time Date</th>\n",
              "      <th>Product</th>\n",
              "      <th>Store</th>\n",
              "      <th>Value</th>\n",
              "      <th>time_str</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2926.000</td>\n",
              "      <td>1012018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2687.531</td>\n",
              "      <td>2012018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.000</td>\n",
              "      <td>3012018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2394.000</td>\n",
              "      <td>4012018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2660.000</td>\n",
              "      <td>5012018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97fee7a3-0cc3-481a-858d-44e606867ad8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-97fee7a3-0cc3-481a-858d-44e606867ad8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-97fee7a3-0cc3-481a-858d-44e606867ad8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d3d1f7e4-858f-43a5-9d7a-9872a9e845db\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d3d1f7e4-858f-43a5-9d7a-9872a9e845db')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d3d1f7e4-858f-43a5-9d7a-9872a9e845db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1080,\n  \"fields\": [\n    {\n      \"column\": \"Time Date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8791548,\n        \"min\": 1012018,\n        \"max\": 31122019,\n        \"num_unique_values\": 1080,\n        \"samples\": [\n          28022019,\n          3072019,\n          2012020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2667437,\n        \"max\": 2667437,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2667437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"QLD_CW_ST0203\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1439.945782927857,\n        \"min\": 2042.8135,\n        \"max\": 8147.7396,\n        \"num_unique_values\": 679,\n        \"samples\": [\n          2740.0128\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_str\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1080,\n        \"samples\": [\n          \"28022019\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the data\n",
        "\n",
        "df['Year'] = df['Time Date'].apply(lambda x: str(x)[-4:])\n",
        "df['Month'] = df['Time Date'].apply(lambda x: str(x)[-6:-4])\n",
        "df['Day'] = df['Time Date'].apply(lambda x: str(x)[:-6])\n",
        "\n"
      ],
      "metadata": {
        "id": "RqVZOqfQ4Tyy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "M3Jb9p5Wu4jP",
        "outputId": "8d3df277-5615-41ba-9bc7-a1e6020293d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time Date  Product          Store     Value time_str  Year Month Day\n",
              "0    1012018  2667437  QLD_CW_ST0203  2926.000  1012018  2018    01   1\n",
              "1    2012018  2667437  QLD_CW_ST0203  2687.531  2012018  2018    01   2\n",
              "2    3012018  2667437  QLD_CW_ST0203  2793.000  3012018  2018    01   3\n",
              "3    4012018  2667437  QLD_CW_ST0203  2394.000  4012018  2018    01   4\n",
              "4    5012018  2667437  QLD_CW_ST0203  2660.000  5012018  2018    01   5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2f96320a-f319-4ac7-bc4d-c8cb4e3f6ef6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time Date</th>\n",
              "      <th>Product</th>\n",
              "      <th>Store</th>\n",
              "      <th>Value</th>\n",
              "      <th>time_str</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2926.000</td>\n",
              "      <td>1012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2687.531</td>\n",
              "      <td>2012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.000</td>\n",
              "      <td>3012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2394.000</td>\n",
              "      <td>4012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2660.000</td>\n",
              "      <td>5012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f96320a-f319-4ac7-bc4d-c8cb4e3f6ef6')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2f96320a-f319-4ac7-bc4d-c8cb4e3f6ef6 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2f96320a-f319-4ac7-bc4d-c8cb4e3f6ef6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-864e2572-dff3-422a-bb0f-1664b544d3f6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-864e2572-dff3-422a-bb0f-1664b544d3f6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-864e2572-dff3-422a-bb0f-1664b544d3f6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1080,\n  \"fields\": [\n    {\n      \"column\": \"Time Date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8791548,\n        \"min\": 1012018,\n        \"max\": 31122019,\n        \"num_unique_values\": 1080,\n        \"samples\": [\n          28022019,\n          3072019,\n          2012020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2667437,\n        \"max\": 2667437,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2667437\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Store\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"QLD_CW_ST0203\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1439.945782927857,\n        \"min\": 2042.8135,\n        \"max\": 8147.7396,\n        \"num_unique_values\": 679,\n        \"samples\": [\n          2740.0128\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_str\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1080,\n        \"samples\": [\n          \"28022019\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"11\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 31,\n        \"samples\": [\n          \"28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ds'] = pd.DatetimeIndex(df['Year']+'-'+df['Month']+'-'+df['Day'])\n",
        "df['y'] = df['Value']"
      ],
      "metadata": {
        "id": "Tqhvo5ciSyEj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "cdd9jKk1TAKY",
        "outputId": "255c4e00-aea5-40e5-8862-54dadd3881cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time Date             int64\n",
              "Product               int64\n",
              "Store                object\n",
              "Value               float64\n",
              "time_str             object\n",
              "Year                 object\n",
              "Month                object\n",
              "Day                  object\n",
              "ds           datetime64[ns]\n",
              "y                   float64\n",
              "dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Time Date</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Store</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Value</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_str</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ds</th>\n",
              "      <td>datetime64[ns]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HzNofvRgvu2N",
        "outputId": "17f45a3b-ab64-4556-ca8f-8de956517ad8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time Date  Product          Store     Value time_str  Year Month Day  \\\n",
              "0    1012018  2667437  QLD_CW_ST0203  2926.000  1012018  2018    01   1   \n",
              "1    2012018  2667437  QLD_CW_ST0203  2687.531  2012018  2018    01   2   \n",
              "2    3012018  2667437  QLD_CW_ST0203  2793.000  3012018  2018    01   3   \n",
              "3    4012018  2667437  QLD_CW_ST0203  2394.000  4012018  2018    01   4   \n",
              "4    5012018  2667437  QLD_CW_ST0203  2660.000  5012018  2018    01   5   \n",
              "\n",
              "          ds         y  \n",
              "0 2018-01-01  2926.000  \n",
              "1 2018-01-02  2687.531  \n",
              "2 2018-01-03  2793.000  \n",
              "3 2018-01-04  2394.000  \n",
              "4 2018-01-05  2660.000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ce2b0170-7dcd-4987-b166-e95820177075\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time Date</th>\n",
              "      <th>Product</th>\n",
              "      <th>Store</th>\n",
              "      <th>Value</th>\n",
              "      <th>time_str</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2926.000</td>\n",
              "      <td>1012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>1</td>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>2926.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2687.531</td>\n",
              "      <td>2012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>2</td>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>2687.531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2793.000</td>\n",
              "      <td>3012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>3</td>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>2793.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2394.000</td>\n",
              "      <td>4012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>4</td>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>2394.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5012018</td>\n",
              "      <td>2667437</td>\n",
              "      <td>QLD_CW_ST0203</td>\n",
              "      <td>2660.000</td>\n",
              "      <td>5012018</td>\n",
              "      <td>2018</td>\n",
              "      <td>01</td>\n",
              "      <td>5</td>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>2660.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce2b0170-7dcd-4987-b166-e95820177075')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ce2b0170-7dcd-4987-b166-e95820177075 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ce2b0170-7dcd-4987-b166-e95820177075');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-66db070e-3bf4-4e6c-9658-6f2e81afa18a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-66db070e-3bf4-4e6c-9658-6f2e81afa18a')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-66db070e-3bf4-4e6c-9658-6f2e81afa18a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.drop(['Time Date','time_str', 'Product', 'Store', 'Year', 'Month', 'Day', 'Value'], axis=1, inplace=True)\n",
        "\n",
        "# df.head()\n"
      ],
      "metadata": {
        "id": "I4w1eTr2SNYb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VnyAMAEYy4eY",
        "outputId": "85cd0876-fcc7-4265-c13c-ac9fbae18162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds         y\n",
              "0 2018-01-01  2926.000\n",
              "1 2018-01-02  2687.531\n",
              "2 2018-01-03  2793.000\n",
              "3 2018-01-04  2394.000\n",
              "4 2018-01-05  2660.000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f0e0e786-b7e4-4009-9c1a-b825302c1e95\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>2926.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>2687.531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>2793.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>2394.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>2660.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0e0e786-b7e4-4009-9c1a-b825302c1e95')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f0e0e786-b7e4-4009-9c1a-b825302c1e95 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f0e0e786-b7e4-4009-9c1a-b825302c1e95');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-cd265681-2a3f-4306-9c72-cbeda2911261\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cd265681-2a3f-4306-9c72-cbeda2911261')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cd265681-2a3f-4306-9c72-cbeda2911261 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1080,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-01 00:00:00\",\n        \"max\": \"2020-12-16 00:00:00\",\n        \"num_unique_values\": 1080,\n        \"samples\": [\n          \"2019-02-28 00:00:00\",\n          \"2019-07-03 00:00:00\",\n          \"2020-01-02 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1439.945782927857,\n        \"min\": 2042.8135,\n        \"max\": 8147.7396,\n        \"num_unique_values\": 679,\n        \"samples\": [\n          2740.0128,\n          2820.93,\n          4909.4689\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SLmeMQkHy4TM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Instatiate model\n",
        "m= Prophet( )\n",
        "m.add_seasonality(name=\"monthly\",period=30,fourier_order=5)\n",
        "m.add_seasonality(name=\"daily\",period=10,fourier_order=2)\n",
        "#Fit\n",
        "model = m.fit(df)\n"
      ],
      "metadata": {
        "id": "DLHrfqGq4U1A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab24111d-51f8-436d-c22a-690058d6ff60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Found custom seasonality named 'daily', disabling built-in 'daily' seasonality.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmp2sep5uaa/26js29td.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmp2sep5uaa/nzjj5w7v.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=79961', 'data', 'file=/tmp/tmp2sep5uaa/26js29td.json', 'init=/tmp/tmp2sep5uaa/nzjj5w7v.json', 'output', 'file=/tmp/tmp2sep5uaa/prophet_model67r89i23/prophet_model-20241125191253.csv', 'method=optimize', 'algorithm=lbfgs', 'iter=10000']\n",
            "19:12:53 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "19:12:53 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Forcast into the future\n",
        "future = m.make_future_dataframe(periods=2*365)\n",
        "\n"
      ],
      "metadata": {
        "id": "s0GjN1hV4dQ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "future"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "U2GxZzU9_kYl",
        "outputId": "4051459d-8bf4-4aea-f718-0c2dd2dd907e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ds\n",
              "0    2018-01-01\n",
              "1    2018-01-02\n",
              "2    2018-01-03\n",
              "3    2018-01-04\n",
              "4    2018-01-05\n",
              "...         ...\n",
              "1805 2022-12-12\n",
              "1806 2022-12-13\n",
              "1807 2022-12-14\n",
              "1808 2022-12-15\n",
              "1809 2022-12-16\n",
              "\n",
              "[1810 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c1f8bc2-144f-4091-b6a5-9dca42c0d95d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1805</th>\n",
              "      <td>2022-12-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1806</th>\n",
              "      <td>2022-12-13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1807</th>\n",
              "      <td>2022-12-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1808</th>\n",
              "      <td>2022-12-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1809</th>\n",
              "      <td>2022-12-16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1810 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c1f8bc2-144f-4091-b6a5-9dca42c0d95d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8c1f8bc2-144f-4091-b6a5-9dca42c0d95d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8c1f8bc2-144f-4091-b6a5-9dca42c0d95d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-be44e194-2a4f-4dd0-9d84-53c4e4a7fedf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-be44e194-2a4f-4dd0-9d84-53c4e4a7fedf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-be44e194-2a4f-4dd0-9d84-53c4e4a7fedf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_dca44286-3c3a-4d5e-bf93-9af66aff1a5e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('future')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_dca44286-3c3a-4d5e-bf93-9af66aff1a5e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('future');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "future",
              "summary": "{\n  \"name\": \"future\",\n  \"rows\": 1810,\n  \"fields\": [\n    {\n      \"column\": \"ds\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-01-01 00:00:00\",\n        \"max\": \"2022-12-16 00:00:00\",\n        \"num_unique_values\": 1810,\n        \"samples\": [\n          \"2021-10-03 00:00:00\",\n          \"2018-03-07 00:00:00\",\n          \"2021-07-14 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "forecast = m.predict(future)\n",
        "forecast.head()[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u4y9dfRMX_S3",
        "outputId": "de089bb0-9b38-4569-b5d8-5a3e268a999d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds         yhat   yhat_lower   yhat_upper\n",
              "0 2018-01-01  2229.868868  1235.847253  3221.600735\n",
              "1 2018-01-02  2282.645324  1308.027000  3346.746628\n",
              "2 2018-01-03  2458.408403  1493.692079  3559.001294\n",
              "3 2018-01-04  2547.359071  1526.101636  3551.064230\n",
              "4 2018-01-05  2412.469965  1405.649305  3404.814771"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d854642c-6618-4ce1-85eb-6a2e1507677e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>2229.868868</td>\n",
              "      <td>1235.847253</td>\n",
              "      <td>3221.600735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-01-02</td>\n",
              "      <td>2282.645324</td>\n",
              "      <td>1308.027000</td>\n",
              "      <td>3346.746628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-01-03</td>\n",
              "      <td>2458.408403</td>\n",
              "      <td>1493.692079</td>\n",
              "      <td>3559.001294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-04</td>\n",
              "      <td>2547.359071</td>\n",
              "      <td>1526.101636</td>\n",
              "      <td>3551.064230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-01-05</td>\n",
              "      <td>2412.469965</td>\n",
              "      <td>1405.649305</td>\n",
              "      <td>3404.814771</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d854642c-6618-4ce1-85eb-6a2e1507677e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d854642c-6618-4ce1-85eb-6a2e1507677e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d854642c-6618-4ce1-85eb-6a2e1507677e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0a2a8edc-8ccf-4d53-94d0-4f28e6c2e0dc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0a2a8edc-8ccf-4d53-94d0-4f28e6c2e0dc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0a2a8edc-8ccf-4d53-94d0-4f28e6c2e0dc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "forecast"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot1 = m.plot(forecast)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "8vmOCzXI4m5J",
        "outputId": "f7f65332-cb77-4820-acf0-24a6788b46fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The black datapoints are the real observation\n",
        "- The line is the predicted y-hat"
      ],
      "metadata": {
        "id": "y4BHBukS4zER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Decompose your data\n",
        "plot2 = m.plot_components(forecast)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2tRoM-h54o89",
        "outputId": "a1513e42-4f80-4366-8014-7dbd7b6cbdd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x1500 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o0e_4QeK4sLX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVph5BexUFWz"
      },
      "source": [
        "## Time Series Forecasting as Supervised Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B66npNjEUFWz"
      },
      "source": [
        "Review:\n",
        "* What is Supervised Learning?\n",
        "    \n",
        "* How is data given in SL?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0adqaxCZUFWz"
      },
      "source": [
        "Supervised learning is where you have input variables (X) and an output variable (y) and you use an algorithm to learn the mapping function from the input to the output.\n",
        "\n",
        "$Y = f(X)$"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can Shift our time series data to create a Y label\n",
        "\n",
        "Lets see this in an example for predicting the Future Price of Any Stock with an LSTM!"
      ],
      "metadata": {
        "id": "BEKBMcOc5H6F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import pandas_datareader as web\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,LSTM, Dropout\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')"
      ],
      "metadata": {
        "id": "CBFB9M6M5GiQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Download data from here! https://ca.finance.yahoo.com/"
      ],
      "metadata": {
        "id": "IlkVzQL0J8In"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/BTC-CAD.csv\")"
      ],
      "metadata": {
        "id": "05RJtrzx5Y9u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sJZbY9eL5Z5k",
        "outputId": "ca06d86b-5ee6-489a-82a3-a4676aa2c9ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date     Price      Open      High       Low     Vol. Change %\n",
              "0  11/25/2024  95,047.4  97,948.8  98,872.4  94,686.1  103.23K   -2.95%\n",
              "1  11/24/2024  97,935.2  97,696.4  98,552.6  95,765.4   77.04K    0.24%\n",
              "2  11/23/2024  97,699.0  98,927.2  98,927.2  97,180.9   72.17K   -1.24%\n",
              "3  11/22/2024  98,929.7  98,381.2  99,617.4  97,182.2  132.23K    0.56%\n",
              "4  11/21/2024  98,374.5  94,308.7  98,937.2  94,063.8  179.07K    4.32%"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9a3e8199-0c58-4be1-a4a1-aa657aa66d8f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Price</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Vol.</th>\n",
              "      <th>Change %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11/25/2024</td>\n",
              "      <td>95,047.4</td>\n",
              "      <td>97,948.8</td>\n",
              "      <td>98,872.4</td>\n",
              "      <td>94,686.1</td>\n",
              "      <td>103.23K</td>\n",
              "      <td>-2.95%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11/24/2024</td>\n",
              "      <td>97,935.2</td>\n",
              "      <td>97,696.4</td>\n",
              "      <td>98,552.6</td>\n",
              "      <td>95,765.4</td>\n",
              "      <td>77.04K</td>\n",
              "      <td>0.24%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11/23/2024</td>\n",
              "      <td>97,699.0</td>\n",
              "      <td>98,927.2</td>\n",
              "      <td>98,927.2</td>\n",
              "      <td>97,180.9</td>\n",
              "      <td>72.17K</td>\n",
              "      <td>-1.24%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11/22/2024</td>\n",
              "      <td>98,929.7</td>\n",
              "      <td>98,381.2</td>\n",
              "      <td>99,617.4</td>\n",
              "      <td>97,182.2</td>\n",
              "      <td>132.23K</td>\n",
              "      <td>0.56%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11/21/2024</td>\n",
              "      <td>98,374.5</td>\n",
              "      <td>94,308.7</td>\n",
              "      <td>98,937.2</td>\n",
              "      <td>94,063.8</td>\n",
              "      <td>179.07K</td>\n",
              "      <td>4.32%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a3e8199-0c58-4be1-a4a1-aa657aa66d8f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9a3e8199-0c58-4be1-a4a1-aa657aa66d8f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9a3e8199-0c58-4be1-a4a1-aa657aa66d8f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bdc8f2c9-632c-4e2f-9ec9-021d7c70dff2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bdc8f2c9-632c-4e2f-9ec9-021d7c70dff2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bdc8f2c9-632c-4e2f-9ec9-021d7c70dff2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1768,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1768,\n        \"samples\": [\n          \"03/27/2022\",\n          \"02/24/2024\",\n          \"10/11/2023\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1764,\n        \"samples\": [\n          \"64,932.6\",\n          \"63,456.8\",\n          \"8,904.8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1762,\n        \"samples\": [\n          \"47,126.0\",\n          \"63,765.8\",\n          \"27,410.3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1764,\n        \"samples\": [\n          \"64,906.0\",\n          \"63,916.7\",\n          \"8,961.8\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1767,\n        \"samples\": [\n          \"11,764.0\",\n          \"62,507.7\",\n          \"9,296.0\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Vol.\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1705,\n        \"samples\": [\n          \"26.67K\",\n          \"318.90K\",\n          \"273.11K\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Change %\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 889,\n        \"samples\": [\n          \"0.00%\",\n          \"2.97%\",\n          \"0.81%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oJrd8oEi5bBD",
        "outputId": "2644987c-50ed-4e74-85bb-88d582d1de2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(365, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Open Price History')\n",
        "plt.plot( df['Date'], df['Open'])\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Open Price CAD($)',fontsize=18)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "9iKeDTR95cKu",
        "outputId": "4c530f90-1d71-42ce-aacf-bf024785e06b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=df.filter(['Close'])\n",
        "dataset=data.values\n",
        "training_data_len= math.ceil(len(dataset) * .8)\n",
        "\n",
        "#scale the data\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "scaled_data=scaler.fit_transform(dataset)\n",
        "\n",
        "scaled_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjqlWVNn5fsK",
        "outputId": "afc84cdb-4bc9-47d3-c81d-97a34375c297"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.26976566],\n",
              "       [0.24307804],\n",
              "       [0.24930401],\n",
              "       [0.2727917 ],\n",
              "       [0.22545382],\n",
              "       [0.2321978 ],\n",
              "       [0.20044499],\n",
              "       [0.18810205],\n",
              "       [0.24577888],\n",
              "       [0.24915359],\n",
              "       [0.22470874],\n",
              "       [0.21468679],\n",
              "       [0.25589065],\n",
              "       [0.2492404 ],\n",
              "       [0.26182319],\n",
              "       [0.27619807],\n",
              "       [0.28161603],\n",
              "       [0.2733586 ],\n",
              "       [0.25042525],\n",
              "       [0.27487897],\n",
              "       [0.31133134],\n",
              "       [0.30840321],\n",
              "       [0.3089253 ],\n",
              "       [0.28011562],\n",
              "       [0.27119075],\n",
              "       [0.27304217],\n",
              "       [0.25607283],\n",
              "       [0.2517727 ],\n",
              "       [0.26158324],\n",
              "       [0.27187992],\n",
              "       [0.26960754],\n",
              "       [0.26143496],\n",
              "       [0.27269018],\n",
              "       [0.27852754],\n",
              "       [0.2649602 ],\n",
              "       [0.25605822],\n",
              "       [0.25050777],\n",
              "       [0.24593846],\n",
              "       [0.24836963],\n",
              "       [0.27309702],\n",
              "       [0.26290643],\n",
              "       [0.30612743],\n",
              "       [0.34485637],\n",
              "       [0.31352764],\n",
              "       [0.33873151],\n",
              "       [0.3538734 ],\n",
              "       [0.34184504],\n",
              "       [0.33269463],\n",
              "       [0.33273165],\n",
              "       [0.32032539],\n",
              "       [0.32647898],\n",
              "       [0.36314062],\n",
              "       [0.372356  ],\n",
              "       [0.35158339],\n",
              "       [0.32771167],\n",
              "       [0.18193939],\n",
              "       [0.01263755],\n",
              "       [0.11063306],\n",
              "       [0.06870983],\n",
              "       [0.0530706 ],\n",
              "       [0.02316463],\n",
              "       [0.0455642 ],\n",
              "       [0.05907085],\n",
              "       [0.04994597],\n",
              "       [0.0501755 ],\n",
              "       [0.05843507],\n",
              "       [0.05935649],\n",
              "       [0.02825963],\n",
              "       [0.        ],\n",
              "       [0.02021321],\n",
              "       [0.04633228],\n",
              "       [0.04579275],\n",
              "       [0.04511664],\n",
              "       [0.0412703 ],\n",
              "       [0.04107088],\n",
              "       [0.03234738],\n",
              "       [0.0554103 ],\n",
              "       [0.08990964],\n",
              "       [0.07806755],\n",
              "       [0.08914517],\n",
              "       [0.07901664],\n",
              "       [0.09008559],\n",
              "       [0.09161385],\n",
              "       [0.10465044],\n",
              "       [0.08848883],\n",
              "       [0.11008876],\n",
              "       [0.11414719],\n",
              "       [0.11392945],\n",
              "       [0.10622898],\n",
              "       [0.11101583],\n",
              "       [0.14342235],\n",
              "       [0.14527639],\n",
              "       [0.12398111],\n",
              "       [0.08172479],\n",
              "       [0.09183851],\n",
              "       [0.08532007],\n",
              "       [0.0598439 ],\n",
              "       [0.08804137],\n",
              "       [0.08331667],\n",
              "       [0.08585882],\n",
              "       [0.08425797],\n",
              "       [0.08771559],\n",
              "       [0.08442973],\n",
              "       [0.08544555],\n",
              "       [0.06887204],\n",
              "       [0.0640316 ],\n",
              "       [0.06624436],\n",
              "       [0.06481342],\n",
              "       [0.06108964],\n",
              "       [0.06338881],\n",
              "       [0.07015305],\n",
              "       [0.07866475],\n",
              "       [0.07543724],\n",
              "       [0.08013369],\n",
              "       [0.07831968],\n",
              "       [0.07852816],\n",
              "       [0.08654905],\n",
              "       [0.08999742],\n",
              "       [0.1101968 ],\n",
              "       [0.14254681],\n",
              "       [0.20028336],\n",
              "       [0.27206755],\n",
              "       [0.34338042],\n",
              "       [0.33660536],\n",
              "       [0.35600306],\n",
              "       [0.35459101],\n",
              "       [0.33385445],\n",
              "       [0.35727471],\n",
              "       [0.45602778],\n",
              "       [0.46278023],\n",
              "       [0.45638669],\n",
              "       [0.47071062],\n",
              "       [0.45096191],\n",
              "       [0.48409773],\n",
              "       [0.4711119 ],\n",
              "       [0.47644948],\n",
              "       [0.47350995],\n",
              "       [0.51895793],\n",
              "       [0.46593691],\n",
              "       [0.47713095],\n",
              "       [0.5119135 ],\n",
              "       [0.50029792],\n",
              "       [0.50975978],\n",
              "       [0.501901  ],\n",
              "       [0.47581964],\n",
              "       [0.46722961],\n",
              "       [0.4950296 ],\n",
              "       [0.4795859 ],\n",
              "       [0.40482906],\n",
              "       [0.3868778 ],\n",
              "       [0.40138342],\n",
              "       [0.39278248],\n",
              "       [0.39168355],\n",
              "       [0.41977815],\n",
              "       [0.56486174],\n",
              "       [0.52874411],\n",
              "       [0.59215523],\n",
              "       [0.59724127],\n",
              "       [0.57714808],\n",
              "       [0.60730598],\n",
              "       [0.59065356],\n",
              "       [0.57548966],\n",
              "       [0.55888565],\n",
              "       [0.51899105],\n",
              "       [0.51755904],\n",
              "       [0.5390327 ],\n",
              "       [0.5339871 ],\n",
              "       [0.5166458 ],\n",
              "       [0.54402902],\n",
              "       [0.53332384],\n",
              "       [0.46026819],\n",
              "       [0.45952291],\n",
              "       [0.46365167],\n",
              "       [0.46448493],\n",
              "       [0.46507872],\n",
              "       [0.43601506],\n",
              "       [0.34603372],\n",
              "       [0.34603372],\n",
              "       [0.33654711],\n",
              "       [0.36742282],\n",
              "       [0.46318716],\n",
              "       [0.59958876],\n",
              "       [0.63082825],\n",
              "       [0.61507465],\n",
              "       [0.65560824],\n",
              "       [0.82475723],\n",
              "       [0.79342928],\n",
              "       [0.85906806],\n",
              "       [0.83479233],\n",
              "       [0.86787856],\n",
              "       [0.80996538],\n",
              "       [0.87988842],\n",
              "       [0.83404647],\n",
              "       [0.83414447],\n",
              "       [0.85826024],\n",
              "       [0.78972898],\n",
              "       [0.79002339],\n",
              "       [0.85950549],\n",
              "       [0.83204112],\n",
              "       [0.86281552],\n",
              "       [0.85826394],\n",
              "       [0.8405726 ],\n",
              "       [0.8037319 ],\n",
              "       [0.82941422],\n",
              "       [0.83216485],\n",
              "       [0.82891756],\n",
              "       [0.8298423 ],\n",
              "       [0.83132585],\n",
              "       [0.84989243],\n",
              "       [0.93842529],\n",
              "       [0.97159258],\n",
              "       [0.96171881],\n",
              "       [0.96409436],\n",
              "       [0.99674297],\n",
              "       [0.98546827],\n",
              "       [0.96224938],\n",
              "       [0.90840865],\n",
              "       [0.97158089],\n",
              "       [0.87759568],\n",
              "       [0.84052428],\n",
              "       [0.79839841],\n",
              "       [0.83902514],\n",
              "       [0.80465469],\n",
              "       [0.79921345],\n",
              "       [0.86407519],\n",
              "       [0.87363253],\n",
              "       [0.94055105],\n",
              "       [0.93940887],\n",
              "       [0.93330418],\n",
              "       [0.92091809],\n",
              "       [0.84011257],\n",
              "       [0.88966164],\n",
              "       [0.91337194],\n",
              "       [0.88714834],\n",
              "       [0.92655495],\n",
              "       [0.88421865],\n",
              "       [0.84015914],\n",
              "       [0.78835903],\n",
              "       [0.78639967],\n",
              "       [0.78354402],\n",
              "       [0.75869564],\n",
              "       [0.76713496],\n",
              "       [0.76584411],\n",
              "       [0.76208691],\n",
              "       [0.76766045],\n",
              "       [0.75866212],\n",
              "       [0.78158446],\n",
              "       [0.74767794],\n",
              "       [0.75120619],\n",
              "       [0.76746074],\n",
              "       [0.74128488],\n",
              "       [0.74966633],\n",
              "       [0.77452387],\n",
              "       [0.72626194],\n",
              "       [0.74298724],\n",
              "       [0.75539263],\n",
              "       [0.76551443],\n",
              "       [0.8476332 ],\n",
              "       [0.82392934],\n",
              "       [0.8204346 ],\n",
              "       [0.78306256],\n",
              "       [0.73974112],\n",
              "       [0.76629673],\n",
              "       [0.75434105],\n",
              "       [0.75765575],\n",
              "       [0.66917491],\n",
              "       [0.7615092 ],\n",
              "       [0.69828201],\n",
              "       [0.70794457],\n",
              "       [0.70689202],\n",
              "       [0.66493878],\n",
              "       [0.66680968],\n",
              "       [0.6678297 ],\n",
              "       [0.66245782],\n",
              "       [0.61134259],\n",
              "       [0.62804216],\n",
              "       [0.67445832],\n",
              "       [0.68652098],\n",
              "       [0.6751658 ],\n",
              "       [0.710423  ],\n",
              "       [0.80979158],\n",
              "       [0.91229016],\n",
              "       [0.90269716],\n",
              "       [0.96325848],\n",
              "       [0.9535891 ],\n",
              "       [0.9427226 ],\n",
              "       [0.92694289],\n",
              "       [0.96124339],\n",
              "       [0.93001618],\n",
              "       [0.95347044],\n",
              "       [0.95509701],\n",
              "       [0.96255314],\n",
              "       [0.96522271],\n",
              "       [1.        ],\n",
              "       [0.97132837],\n",
              "       [0.96318483],\n",
              "       [0.93499583],\n",
              "       [0.9537744 ],\n",
              "       [0.95047606],\n",
              "       [0.93945252],\n",
              "       [0.95497776],\n",
              "       [0.96167887],\n",
              "       [0.9398122 ],\n",
              "       [0.99992557],\n",
              "       [0.9429418 ],\n",
              "       [0.94087021],\n",
              "       [0.93718569],\n",
              "       [0.9262091 ],\n",
              "       [0.90195013],\n",
              "       [0.90529659],\n",
              "       [0.89882559],\n",
              "       [0.91487283],\n",
              "       [0.90583553],\n",
              "       [0.92574206],\n",
              "       [0.85854296],\n",
              "       [0.86372038],\n",
              "       [0.87460227],\n",
              "       [0.86845433],\n",
              "       [0.87889432],\n",
              "       [0.88138444],\n",
              "       [0.87628242],\n",
              "       [0.86481638],\n",
              "       [0.90531276],\n",
              "       [0.88145984],\n",
              "       [0.88425275],\n",
              "       [0.88122369],\n",
              "       [0.87999013],\n",
              "       [0.87870162],\n",
              "       [0.88741703],\n",
              "       [0.93448241],\n",
              "       [0.91983553],\n",
              "       [0.91469104],\n",
              "       [0.91179506],\n",
              "       [0.91301849],\n",
              "       [0.9042452 ],\n",
              "       [0.91542463],\n",
              "       [0.9040073 ],\n",
              "       [0.87911917],\n",
              "       [0.7427205 ],\n",
              "       [0.70410749],\n",
              "       [0.70726475],\n",
              "       [0.7101621 ],\n",
              "       [0.70631216],\n",
              "       [0.70051981],\n",
              "       [0.72415275],\n",
              "       [0.7141572 ],\n",
              "       [0.70943669],\n",
              "       [0.70677491],\n",
              "       [0.71035987],\n",
              "       [0.71214523],\n",
              "       [0.81637461],\n",
              "       [0.78403211],\n",
              "       [0.68863037],\n",
              "       [0.68739525],\n",
              "       [0.69213193],\n",
              "       [0.69978583],\n",
              "       [0.69204016],\n",
              "       [0.69508266],\n",
              "       [0.69319227],\n",
              "       [0.73205157],\n",
              "       [0.70612706],\n",
              "       [0.70544744],\n",
              "       [0.69719673],\n",
              "       [0.64554801],\n",
              "       [0.71859401]])"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "id": "CtWIxq80KdNw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "af41973c-d53b-4d73-9cf5-e896c38b425d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close\n",
              "0    26637.658203\n",
              "1    26102.625000\n",
              "2    26227.443359\n",
              "3    26698.324219\n",
              "4    25749.294922\n",
              "..            ...\n",
              "360  35385.828125\n",
              "361  35372.203125\n",
              "362  35206.792969\n",
              "363  34171.339844\n",
              "364  35635.765625\n",
              "\n",
              "[365 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-521856f4-da31-4ec3-a702-aa7ec8a93a1d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26637.658203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26102.625000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26227.443359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26698.324219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25749.294922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>35385.828125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>35372.203125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>35206.792969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>34171.339844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>364</th>\n",
              "      <td>35635.765625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>365 rows × 1 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-521856f4-da31-4ec3-a702-aa7ec8a93a1d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-521856f4-da31-4ec3-a702-aa7ec8a93a1d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-521856f4-da31-4ec3-a702-aa7ec8a93a1d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c5a2a0ac-10a5-4626-9379-748401772d9b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5a2a0ac-10a5-4626-9379-748401772d9b')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c5a2a0ac-10a5-4626-9379-748401772d9b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create training data set\n",
        "#This is where we shift the data\n",
        "train_data=scaled_data[0:training_data_len,:]\n",
        "\n",
        "#split the data into x_train and y_train data sets\n",
        "\n",
        "x_train =[]\n",
        "y_train=[]\n",
        "\n",
        "numDays = 14\n",
        "\n",
        "for i in range(numDays,len(train_data) -1):\n",
        "  x_train.append(train_data[i-numDays:i, 0])\n",
        "  y_train.append(train_data[i + 1,0])"
      ],
      "metadata": {
        "id": "_P2-6WqT5i9g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train_data[0:7, 0]"
      ],
      "metadata": {
        "id": "sGrAnTf3feeT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# len(x_train)\n",
        "x_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ZORVNnQLKWu",
        "outputId": "072669ef-38c1-47c3-ce71-9af4f63e0ee8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.26976566, 0.24307804, 0.24930401, 0.2727917 , 0.22545382,\n",
              "        0.2321978 , 0.20044499, 0.18810205, 0.24577888, 0.24915359,\n",
              "        0.22470874, 0.21468679, 0.25589065, 0.2492404 ]),\n",
              " array([0.24307804, 0.24930401, 0.2727917 , 0.22545382, 0.2321978 ,\n",
              "        0.20044499, 0.18810205, 0.24577888, 0.24915359, 0.22470874,\n",
              "        0.21468679, 0.25589065, 0.2492404 , 0.26182319]),\n",
              " array([0.24930401, 0.2727917 , 0.22545382, 0.2321978 , 0.20044499,\n",
              "        0.18810205, 0.24577888, 0.24915359, 0.22470874, 0.21468679,\n",
              "        0.25589065, 0.2492404 , 0.26182319, 0.27619807]),\n",
              " array([0.2727917 , 0.22545382, 0.2321978 , 0.20044499, 0.18810205,\n",
              "        0.24577888, 0.24915359, 0.22470874, 0.21468679, 0.25589065,\n",
              "        0.2492404 , 0.26182319, 0.27619807, 0.28161603]),\n",
              " array([0.22545382, 0.2321978 , 0.20044499, 0.18810205, 0.24577888,\n",
              "        0.24915359, 0.22470874, 0.21468679, 0.25589065, 0.2492404 ,\n",
              "        0.26182319, 0.27619807, 0.28161603, 0.2733586 ]),\n",
              " array([0.2321978 , 0.20044499, 0.18810205, 0.24577888, 0.24915359,\n",
              "        0.22470874, 0.21468679, 0.25589065, 0.2492404 , 0.26182319,\n",
              "        0.27619807, 0.28161603, 0.2733586 , 0.25042525]),\n",
              " array([0.20044499, 0.18810205, 0.24577888, 0.24915359, 0.22470874,\n",
              "        0.21468679, 0.25589065, 0.2492404 , 0.26182319, 0.27619807,\n",
              "        0.28161603, 0.2733586 , 0.25042525, 0.27487897]),\n",
              " array([0.18810205, 0.24577888, 0.24915359, 0.22470874, 0.21468679,\n",
              "        0.25589065, 0.2492404 , 0.26182319, 0.27619807, 0.28161603,\n",
              "        0.2733586 , 0.25042525, 0.27487897, 0.31133134]),\n",
              " array([0.24577888, 0.24915359, 0.22470874, 0.21468679, 0.25589065,\n",
              "        0.2492404 , 0.26182319, 0.27619807, 0.28161603, 0.2733586 ,\n",
              "        0.25042525, 0.27487897, 0.31133134, 0.30840321]),\n",
              " array([0.24915359, 0.22470874, 0.21468679, 0.25589065, 0.2492404 ,\n",
              "        0.26182319, 0.27619807, 0.28161603, 0.2733586 , 0.25042525,\n",
              "        0.27487897, 0.31133134, 0.30840321, 0.3089253 ]),\n",
              " array([0.22470874, 0.21468679, 0.25589065, 0.2492404 , 0.26182319,\n",
              "        0.27619807, 0.28161603, 0.2733586 , 0.25042525, 0.27487897,\n",
              "        0.31133134, 0.30840321, 0.3089253 , 0.28011562]),\n",
              " array([0.21468679, 0.25589065, 0.2492404 , 0.26182319, 0.27619807,\n",
              "        0.28161603, 0.2733586 , 0.25042525, 0.27487897, 0.31133134,\n",
              "        0.30840321, 0.3089253 , 0.28011562, 0.27119075]),\n",
              " array([0.25589065, 0.2492404 , 0.26182319, 0.27619807, 0.28161603,\n",
              "        0.2733586 , 0.25042525, 0.27487897, 0.31133134, 0.30840321,\n",
              "        0.3089253 , 0.28011562, 0.27119075, 0.27304217]),\n",
              " array([0.2492404 , 0.26182319, 0.27619807, 0.28161603, 0.2733586 ,\n",
              "        0.25042525, 0.27487897, 0.31133134, 0.30840321, 0.3089253 ,\n",
              "        0.28011562, 0.27119075, 0.27304217, 0.25607283]),\n",
              " array([0.26182319, 0.27619807, 0.28161603, 0.2733586 , 0.25042525,\n",
              "        0.27487897, 0.31133134, 0.30840321, 0.3089253 , 0.28011562,\n",
              "        0.27119075, 0.27304217, 0.25607283, 0.2517727 ]),\n",
              " array([0.27619807, 0.28161603, 0.2733586 , 0.25042525, 0.27487897,\n",
              "        0.31133134, 0.30840321, 0.3089253 , 0.28011562, 0.27119075,\n",
              "        0.27304217, 0.25607283, 0.2517727 , 0.26158324]),\n",
              " array([0.28161603, 0.2733586 , 0.25042525, 0.27487897, 0.31133134,\n",
              "        0.30840321, 0.3089253 , 0.28011562, 0.27119075, 0.27304217,\n",
              "        0.25607283, 0.2517727 , 0.26158324, 0.27187992]),\n",
              " array([0.2733586 , 0.25042525, 0.27487897, 0.31133134, 0.30840321,\n",
              "        0.3089253 , 0.28011562, 0.27119075, 0.27304217, 0.25607283,\n",
              "        0.2517727 , 0.26158324, 0.27187992, 0.26960754]),\n",
              " array([0.25042525, 0.27487897, 0.31133134, 0.30840321, 0.3089253 ,\n",
              "        0.28011562, 0.27119075, 0.27304217, 0.25607283, 0.2517727 ,\n",
              "        0.26158324, 0.27187992, 0.26960754, 0.26143496]),\n",
              " array([0.27487897, 0.31133134, 0.30840321, 0.3089253 , 0.28011562,\n",
              "        0.27119075, 0.27304217, 0.25607283, 0.2517727 , 0.26158324,\n",
              "        0.27187992, 0.26960754, 0.26143496, 0.27269018]),\n",
              " array([0.31133134, 0.30840321, 0.3089253 , 0.28011562, 0.27119075,\n",
              "        0.27304217, 0.25607283, 0.2517727 , 0.26158324, 0.27187992,\n",
              "        0.26960754, 0.26143496, 0.27269018, 0.27852754]),\n",
              " array([0.30840321, 0.3089253 , 0.28011562, 0.27119075, 0.27304217,\n",
              "        0.25607283, 0.2517727 , 0.26158324, 0.27187992, 0.26960754,\n",
              "        0.26143496, 0.27269018, 0.27852754, 0.2649602 ]),\n",
              " array([0.3089253 , 0.28011562, 0.27119075, 0.27304217, 0.25607283,\n",
              "        0.2517727 , 0.26158324, 0.27187992, 0.26960754, 0.26143496,\n",
              "        0.27269018, 0.27852754, 0.2649602 , 0.25605822]),\n",
              " array([0.28011562, 0.27119075, 0.27304217, 0.25607283, 0.2517727 ,\n",
              "        0.26158324, 0.27187992, 0.26960754, 0.26143496, 0.27269018,\n",
              "        0.27852754, 0.2649602 , 0.25605822, 0.25050777]),\n",
              " array([0.27119075, 0.27304217, 0.25607283, 0.2517727 , 0.26158324,\n",
              "        0.27187992, 0.26960754, 0.26143496, 0.27269018, 0.27852754,\n",
              "        0.2649602 , 0.25605822, 0.25050777, 0.24593846]),\n",
              " array([0.27304217, 0.25607283, 0.2517727 , 0.26158324, 0.27187992,\n",
              "        0.26960754, 0.26143496, 0.27269018, 0.27852754, 0.2649602 ,\n",
              "        0.25605822, 0.25050777, 0.24593846, 0.24836963]),\n",
              " array([0.25607283, 0.2517727 , 0.26158324, 0.27187992, 0.26960754,\n",
              "        0.26143496, 0.27269018, 0.27852754, 0.2649602 , 0.25605822,\n",
              "        0.25050777, 0.24593846, 0.24836963, 0.27309702]),\n",
              " array([0.2517727 , 0.26158324, 0.27187992, 0.26960754, 0.26143496,\n",
              "        0.27269018, 0.27852754, 0.2649602 , 0.25605822, 0.25050777,\n",
              "        0.24593846, 0.24836963, 0.27309702, 0.26290643]),\n",
              " array([0.26158324, 0.27187992, 0.26960754, 0.26143496, 0.27269018,\n",
              "        0.27852754, 0.2649602 , 0.25605822, 0.25050777, 0.24593846,\n",
              "        0.24836963, 0.27309702, 0.26290643, 0.30612743]),\n",
              " array([0.27187992, 0.26960754, 0.26143496, 0.27269018, 0.27852754,\n",
              "        0.2649602 , 0.25605822, 0.25050777, 0.24593846, 0.24836963,\n",
              "        0.27309702, 0.26290643, 0.30612743, 0.34485637]),\n",
              " array([0.26960754, 0.26143496, 0.27269018, 0.27852754, 0.2649602 ,\n",
              "        0.25605822, 0.25050777, 0.24593846, 0.24836963, 0.27309702,\n",
              "        0.26290643, 0.30612743, 0.34485637, 0.31352764]),\n",
              " array([0.26143496, 0.27269018, 0.27852754, 0.2649602 , 0.25605822,\n",
              "        0.25050777, 0.24593846, 0.24836963, 0.27309702, 0.26290643,\n",
              "        0.30612743, 0.34485637, 0.31352764, 0.33873151]),\n",
              " array([0.27269018, 0.27852754, 0.2649602 , 0.25605822, 0.25050777,\n",
              "        0.24593846, 0.24836963, 0.27309702, 0.26290643, 0.30612743,\n",
              "        0.34485637, 0.31352764, 0.33873151, 0.3538734 ]),\n",
              " array([0.27852754, 0.2649602 , 0.25605822, 0.25050777, 0.24593846,\n",
              "        0.24836963, 0.27309702, 0.26290643, 0.30612743, 0.34485637,\n",
              "        0.31352764, 0.33873151, 0.3538734 , 0.34184504]),\n",
              " array([0.2649602 , 0.25605822, 0.25050777, 0.24593846, 0.24836963,\n",
              "        0.27309702, 0.26290643, 0.30612743, 0.34485637, 0.31352764,\n",
              "        0.33873151, 0.3538734 , 0.34184504, 0.33269463]),\n",
              " array([0.25605822, 0.25050777, 0.24593846, 0.24836963, 0.27309702,\n",
              "        0.26290643, 0.30612743, 0.34485637, 0.31352764, 0.33873151,\n",
              "        0.3538734 , 0.34184504, 0.33269463, 0.33273165]),\n",
              " array([0.25050777, 0.24593846, 0.24836963, 0.27309702, 0.26290643,\n",
              "        0.30612743, 0.34485637, 0.31352764, 0.33873151, 0.3538734 ,\n",
              "        0.34184504, 0.33269463, 0.33273165, 0.32032539]),\n",
              " array([0.24593846, 0.24836963, 0.27309702, 0.26290643, 0.30612743,\n",
              "        0.34485637, 0.31352764, 0.33873151, 0.3538734 , 0.34184504,\n",
              "        0.33269463, 0.33273165, 0.32032539, 0.32647898]),\n",
              " array([0.24836963, 0.27309702, 0.26290643, 0.30612743, 0.34485637,\n",
              "        0.31352764, 0.33873151, 0.3538734 , 0.34184504, 0.33269463,\n",
              "        0.33273165, 0.32032539, 0.32647898, 0.36314062]),\n",
              " array([0.27309702, 0.26290643, 0.30612743, 0.34485637, 0.31352764,\n",
              "        0.33873151, 0.3538734 , 0.34184504, 0.33269463, 0.33273165,\n",
              "        0.32032539, 0.32647898, 0.36314062, 0.372356  ]),\n",
              " array([0.26290643, 0.30612743, 0.34485637, 0.31352764, 0.33873151,\n",
              "        0.3538734 , 0.34184504, 0.33269463, 0.33273165, 0.32032539,\n",
              "        0.32647898, 0.36314062, 0.372356  , 0.35158339]),\n",
              " array([0.30612743, 0.34485637, 0.31352764, 0.33873151, 0.3538734 ,\n",
              "        0.34184504, 0.33269463, 0.33273165, 0.32032539, 0.32647898,\n",
              "        0.36314062, 0.372356  , 0.35158339, 0.32771167]),\n",
              " array([0.34485637, 0.31352764, 0.33873151, 0.3538734 , 0.34184504,\n",
              "        0.33269463, 0.33273165, 0.32032539, 0.32647898, 0.36314062,\n",
              "        0.372356  , 0.35158339, 0.32771167, 0.18193939]),\n",
              " array([0.31352764, 0.33873151, 0.3538734 , 0.34184504, 0.33269463,\n",
              "        0.33273165, 0.32032539, 0.32647898, 0.36314062, 0.372356  ,\n",
              "        0.35158339, 0.32771167, 0.18193939, 0.01263755]),\n",
              " array([0.33873151, 0.3538734 , 0.34184504, 0.33269463, 0.33273165,\n",
              "        0.32032539, 0.32647898, 0.36314062, 0.372356  , 0.35158339,\n",
              "        0.32771167, 0.18193939, 0.01263755, 0.11063306]),\n",
              " array([0.3538734 , 0.34184504, 0.33269463, 0.33273165, 0.32032539,\n",
              "        0.32647898, 0.36314062, 0.372356  , 0.35158339, 0.32771167,\n",
              "        0.18193939, 0.01263755, 0.11063306, 0.06870983]),\n",
              " array([0.34184504, 0.33269463, 0.33273165, 0.32032539, 0.32647898,\n",
              "        0.36314062, 0.372356  , 0.35158339, 0.32771167, 0.18193939,\n",
              "        0.01263755, 0.11063306, 0.06870983, 0.0530706 ]),\n",
              " array([0.33269463, 0.33273165, 0.32032539, 0.32647898, 0.36314062,\n",
              "        0.372356  , 0.35158339, 0.32771167, 0.18193939, 0.01263755,\n",
              "        0.11063306, 0.06870983, 0.0530706 , 0.02316463]),\n",
              " array([0.33273165, 0.32032539, 0.32647898, 0.36314062, 0.372356  ,\n",
              "        0.35158339, 0.32771167, 0.18193939, 0.01263755, 0.11063306,\n",
              "        0.06870983, 0.0530706 , 0.02316463, 0.0455642 ]),\n",
              " array([0.32032539, 0.32647898, 0.36314062, 0.372356  , 0.35158339,\n",
              "        0.32771167, 0.18193939, 0.01263755, 0.11063306, 0.06870983,\n",
              "        0.0530706 , 0.02316463, 0.0455642 , 0.05907085]),\n",
              " array([0.32647898, 0.36314062, 0.372356  , 0.35158339, 0.32771167,\n",
              "        0.18193939, 0.01263755, 0.11063306, 0.06870983, 0.0530706 ,\n",
              "        0.02316463, 0.0455642 , 0.05907085, 0.04994597]),\n",
              " array([0.36314062, 0.372356  , 0.35158339, 0.32771167, 0.18193939,\n",
              "        0.01263755, 0.11063306, 0.06870983, 0.0530706 , 0.02316463,\n",
              "        0.0455642 , 0.05907085, 0.04994597, 0.0501755 ]),\n",
              " array([0.372356  , 0.35158339, 0.32771167, 0.18193939, 0.01263755,\n",
              "        0.11063306, 0.06870983, 0.0530706 , 0.02316463, 0.0455642 ,\n",
              "        0.05907085, 0.04994597, 0.0501755 , 0.05843507]),\n",
              " array([0.35158339, 0.32771167, 0.18193939, 0.01263755, 0.11063306,\n",
              "        0.06870983, 0.0530706 , 0.02316463, 0.0455642 , 0.05907085,\n",
              "        0.04994597, 0.0501755 , 0.05843507, 0.05935649]),\n",
              " array([0.32771167, 0.18193939, 0.01263755, 0.11063306, 0.06870983,\n",
              "        0.0530706 , 0.02316463, 0.0455642 , 0.05907085, 0.04994597,\n",
              "        0.0501755 , 0.05843507, 0.05935649, 0.02825963]),\n",
              " array([0.18193939, 0.01263755, 0.11063306, 0.06870983, 0.0530706 ,\n",
              "        0.02316463, 0.0455642 , 0.05907085, 0.04994597, 0.0501755 ,\n",
              "        0.05843507, 0.05935649, 0.02825963, 0.        ]),\n",
              " array([0.01263755, 0.11063306, 0.06870983, 0.0530706 , 0.02316463,\n",
              "        0.0455642 , 0.05907085, 0.04994597, 0.0501755 , 0.05843507,\n",
              "        0.05935649, 0.02825963, 0.        , 0.02021321]),\n",
              " array([0.11063306, 0.06870983, 0.0530706 , 0.02316463, 0.0455642 ,\n",
              "        0.05907085, 0.04994597, 0.0501755 , 0.05843507, 0.05935649,\n",
              "        0.02825963, 0.        , 0.02021321, 0.04633228]),\n",
              " array([0.06870983, 0.0530706 , 0.02316463, 0.0455642 , 0.05907085,\n",
              "        0.04994597, 0.0501755 , 0.05843507, 0.05935649, 0.02825963,\n",
              "        0.        , 0.02021321, 0.04633228, 0.04579275]),\n",
              " array([0.0530706 , 0.02316463, 0.0455642 , 0.05907085, 0.04994597,\n",
              "        0.0501755 , 0.05843507, 0.05935649, 0.02825963, 0.        ,\n",
              "        0.02021321, 0.04633228, 0.04579275, 0.04511664]),\n",
              " array([0.02316463, 0.0455642 , 0.05907085, 0.04994597, 0.0501755 ,\n",
              "        0.05843507, 0.05935649, 0.02825963, 0.        , 0.02021321,\n",
              "        0.04633228, 0.04579275, 0.04511664, 0.0412703 ]),\n",
              " array([0.0455642 , 0.05907085, 0.04994597, 0.0501755 , 0.05843507,\n",
              "        0.05935649, 0.02825963, 0.        , 0.02021321, 0.04633228,\n",
              "        0.04579275, 0.04511664, 0.0412703 , 0.04107088]),\n",
              " array([0.05907085, 0.04994597, 0.0501755 , 0.05843507, 0.05935649,\n",
              "        0.02825963, 0.        , 0.02021321, 0.04633228, 0.04579275,\n",
              "        0.04511664, 0.0412703 , 0.04107088, 0.03234738]),\n",
              " array([0.04994597, 0.0501755 , 0.05843507, 0.05935649, 0.02825963,\n",
              "        0.        , 0.02021321, 0.04633228, 0.04579275, 0.04511664,\n",
              "        0.0412703 , 0.04107088, 0.03234738, 0.0554103 ]),\n",
              " array([0.0501755 , 0.05843507, 0.05935649, 0.02825963, 0.        ,\n",
              "        0.02021321, 0.04633228, 0.04579275, 0.04511664, 0.0412703 ,\n",
              "        0.04107088, 0.03234738, 0.0554103 , 0.08990964]),\n",
              " array([0.05843507, 0.05935649, 0.02825963, 0.        , 0.02021321,\n",
              "        0.04633228, 0.04579275, 0.04511664, 0.0412703 , 0.04107088,\n",
              "        0.03234738, 0.0554103 , 0.08990964, 0.07806755]),\n",
              " array([0.05935649, 0.02825963, 0.        , 0.02021321, 0.04633228,\n",
              "        0.04579275, 0.04511664, 0.0412703 , 0.04107088, 0.03234738,\n",
              "        0.0554103 , 0.08990964, 0.07806755, 0.08914517]),\n",
              " array([0.02825963, 0.        , 0.02021321, 0.04633228, 0.04579275,\n",
              "        0.04511664, 0.0412703 , 0.04107088, 0.03234738, 0.0554103 ,\n",
              "        0.08990964, 0.07806755, 0.08914517, 0.07901664]),\n",
              " array([0.        , 0.02021321, 0.04633228, 0.04579275, 0.04511664,\n",
              "        0.0412703 , 0.04107088, 0.03234738, 0.0554103 , 0.08990964,\n",
              "        0.07806755, 0.08914517, 0.07901664, 0.09008559]),\n",
              " array([0.02021321, 0.04633228, 0.04579275, 0.04511664, 0.0412703 ,\n",
              "        0.04107088, 0.03234738, 0.0554103 , 0.08990964, 0.07806755,\n",
              "        0.08914517, 0.07901664, 0.09008559, 0.09161385]),\n",
              " array([0.04633228, 0.04579275, 0.04511664, 0.0412703 , 0.04107088,\n",
              "        0.03234738, 0.0554103 , 0.08990964, 0.07806755, 0.08914517,\n",
              "        0.07901664, 0.09008559, 0.09161385, 0.10465044]),\n",
              " array([0.04579275, 0.04511664, 0.0412703 , 0.04107088, 0.03234738,\n",
              "        0.0554103 , 0.08990964, 0.07806755, 0.08914517, 0.07901664,\n",
              "        0.09008559, 0.09161385, 0.10465044, 0.08848883]),\n",
              " array([0.04511664, 0.0412703 , 0.04107088, 0.03234738, 0.0554103 ,\n",
              "        0.08990964, 0.07806755, 0.08914517, 0.07901664, 0.09008559,\n",
              "        0.09161385, 0.10465044, 0.08848883, 0.11008876]),\n",
              " array([0.0412703 , 0.04107088, 0.03234738, 0.0554103 , 0.08990964,\n",
              "        0.07806755, 0.08914517, 0.07901664, 0.09008559, 0.09161385,\n",
              "        0.10465044, 0.08848883, 0.11008876, 0.11414719]),\n",
              " array([0.04107088, 0.03234738, 0.0554103 , 0.08990964, 0.07806755,\n",
              "        0.08914517, 0.07901664, 0.09008559, 0.09161385, 0.10465044,\n",
              "        0.08848883, 0.11008876, 0.11414719, 0.11392945]),\n",
              " array([0.03234738, 0.0554103 , 0.08990964, 0.07806755, 0.08914517,\n",
              "        0.07901664, 0.09008559, 0.09161385, 0.10465044, 0.08848883,\n",
              "        0.11008876, 0.11414719, 0.11392945, 0.10622898]),\n",
              " array([0.0554103 , 0.08990964, 0.07806755, 0.08914517, 0.07901664,\n",
              "        0.09008559, 0.09161385, 0.10465044, 0.08848883, 0.11008876,\n",
              "        0.11414719, 0.11392945, 0.10622898, 0.11101583]),\n",
              " array([0.08990964, 0.07806755, 0.08914517, 0.07901664, 0.09008559,\n",
              "        0.09161385, 0.10465044, 0.08848883, 0.11008876, 0.11414719,\n",
              "        0.11392945, 0.10622898, 0.11101583, 0.14342235]),\n",
              " array([0.07806755, 0.08914517, 0.07901664, 0.09008559, 0.09161385,\n",
              "        0.10465044, 0.08848883, 0.11008876, 0.11414719, 0.11392945,\n",
              "        0.10622898, 0.11101583, 0.14342235, 0.14527639]),\n",
              " array([0.08914517, 0.07901664, 0.09008559, 0.09161385, 0.10465044,\n",
              "        0.08848883, 0.11008876, 0.11414719, 0.11392945, 0.10622898,\n",
              "        0.11101583, 0.14342235, 0.14527639, 0.12398111]),\n",
              " array([0.07901664, 0.09008559, 0.09161385, 0.10465044, 0.08848883,\n",
              "        0.11008876, 0.11414719, 0.11392945, 0.10622898, 0.11101583,\n",
              "        0.14342235, 0.14527639, 0.12398111, 0.08172479]),\n",
              " array([0.09008559, 0.09161385, 0.10465044, 0.08848883, 0.11008876,\n",
              "        0.11414719, 0.11392945, 0.10622898, 0.11101583, 0.14342235,\n",
              "        0.14527639, 0.12398111, 0.08172479, 0.09183851]),\n",
              " array([0.09161385, 0.10465044, 0.08848883, 0.11008876, 0.11414719,\n",
              "        0.11392945, 0.10622898, 0.11101583, 0.14342235, 0.14527639,\n",
              "        0.12398111, 0.08172479, 0.09183851, 0.08532007]),\n",
              " array([0.10465044, 0.08848883, 0.11008876, 0.11414719, 0.11392945,\n",
              "        0.10622898, 0.11101583, 0.14342235, 0.14527639, 0.12398111,\n",
              "        0.08172479, 0.09183851, 0.08532007, 0.0598439 ]),\n",
              " array([0.08848883, 0.11008876, 0.11414719, 0.11392945, 0.10622898,\n",
              "        0.11101583, 0.14342235, 0.14527639, 0.12398111, 0.08172479,\n",
              "        0.09183851, 0.08532007, 0.0598439 , 0.08804137]),\n",
              " array([0.11008876, 0.11414719, 0.11392945, 0.10622898, 0.11101583,\n",
              "        0.14342235, 0.14527639, 0.12398111, 0.08172479, 0.09183851,\n",
              "        0.08532007, 0.0598439 , 0.08804137, 0.08331667]),\n",
              " array([0.11414719, 0.11392945, 0.10622898, 0.11101583, 0.14342235,\n",
              "        0.14527639, 0.12398111, 0.08172479, 0.09183851, 0.08532007,\n",
              "        0.0598439 , 0.08804137, 0.08331667, 0.08585882]),\n",
              " array([0.11392945, 0.10622898, 0.11101583, 0.14342235, 0.14527639,\n",
              "        0.12398111, 0.08172479, 0.09183851, 0.08532007, 0.0598439 ,\n",
              "        0.08804137, 0.08331667, 0.08585882, 0.08425797]),\n",
              " array([0.10622898, 0.11101583, 0.14342235, 0.14527639, 0.12398111,\n",
              "        0.08172479, 0.09183851, 0.08532007, 0.0598439 , 0.08804137,\n",
              "        0.08331667, 0.08585882, 0.08425797, 0.08771559]),\n",
              " array([0.11101583, 0.14342235, 0.14527639, 0.12398111, 0.08172479,\n",
              "        0.09183851, 0.08532007, 0.0598439 , 0.08804137, 0.08331667,\n",
              "        0.08585882, 0.08425797, 0.08771559, 0.08442973]),\n",
              " array([0.14342235, 0.14527639, 0.12398111, 0.08172479, 0.09183851,\n",
              "        0.08532007, 0.0598439 , 0.08804137, 0.08331667, 0.08585882,\n",
              "        0.08425797, 0.08771559, 0.08442973, 0.08544555]),\n",
              " array([0.14527639, 0.12398111, 0.08172479, 0.09183851, 0.08532007,\n",
              "        0.0598439 , 0.08804137, 0.08331667, 0.08585882, 0.08425797,\n",
              "        0.08771559, 0.08442973, 0.08544555, 0.06887204]),\n",
              " array([0.12398111, 0.08172479, 0.09183851, 0.08532007, 0.0598439 ,\n",
              "        0.08804137, 0.08331667, 0.08585882, 0.08425797, 0.08771559,\n",
              "        0.08442973, 0.08544555, 0.06887204, 0.0640316 ]),\n",
              " array([0.08172479, 0.09183851, 0.08532007, 0.0598439 , 0.08804137,\n",
              "        0.08331667, 0.08585882, 0.08425797, 0.08771559, 0.08442973,\n",
              "        0.08544555, 0.06887204, 0.0640316 , 0.06624436]),\n",
              " array([0.09183851, 0.08532007, 0.0598439 , 0.08804137, 0.08331667,\n",
              "        0.08585882, 0.08425797, 0.08771559, 0.08442973, 0.08544555,\n",
              "        0.06887204, 0.0640316 , 0.06624436, 0.06481342]),\n",
              " array([0.08532007, 0.0598439 , 0.08804137, 0.08331667, 0.08585882,\n",
              "        0.08425797, 0.08771559, 0.08442973, 0.08544555, 0.06887204,\n",
              "        0.0640316 , 0.06624436, 0.06481342, 0.06108964]),\n",
              " array([0.0598439 , 0.08804137, 0.08331667, 0.08585882, 0.08425797,\n",
              "        0.08771559, 0.08442973, 0.08544555, 0.06887204, 0.0640316 ,\n",
              "        0.06624436, 0.06481342, 0.06108964, 0.06338881]),\n",
              " array([0.08804137, 0.08331667, 0.08585882, 0.08425797, 0.08771559,\n",
              "        0.08442973, 0.08544555, 0.06887204, 0.0640316 , 0.06624436,\n",
              "        0.06481342, 0.06108964, 0.06338881, 0.07015305]),\n",
              " array([0.08331667, 0.08585882, 0.08425797, 0.08771559, 0.08442973,\n",
              "        0.08544555, 0.06887204, 0.0640316 , 0.06624436, 0.06481342,\n",
              "        0.06108964, 0.06338881, 0.07015305, 0.07866475]),\n",
              " array([0.08585882, 0.08425797, 0.08771559, 0.08442973, 0.08544555,\n",
              "        0.06887204, 0.0640316 , 0.06624436, 0.06481342, 0.06108964,\n",
              "        0.06338881, 0.07015305, 0.07866475, 0.07543724]),\n",
              " array([0.08425797, 0.08771559, 0.08442973, 0.08544555, 0.06887204,\n",
              "        0.0640316 , 0.06624436, 0.06481342, 0.06108964, 0.06338881,\n",
              "        0.07015305, 0.07866475, 0.07543724, 0.08013369]),\n",
              " array([0.08771559, 0.08442973, 0.08544555, 0.06887204, 0.0640316 ,\n",
              "        0.06624436, 0.06481342, 0.06108964, 0.06338881, 0.07015305,\n",
              "        0.07866475, 0.07543724, 0.08013369, 0.07831968]),\n",
              " array([0.08442973, 0.08544555, 0.06887204, 0.0640316 , 0.06624436,\n",
              "        0.06481342, 0.06108964, 0.06338881, 0.07015305, 0.07866475,\n",
              "        0.07543724, 0.08013369, 0.07831968, 0.07852816]),\n",
              " array([0.08544555, 0.06887204, 0.0640316 , 0.06624436, 0.06481342,\n",
              "        0.06108964, 0.06338881, 0.07015305, 0.07866475, 0.07543724,\n",
              "        0.08013369, 0.07831968, 0.07852816, 0.08654905]),\n",
              " array([0.06887204, 0.0640316 , 0.06624436, 0.06481342, 0.06108964,\n",
              "        0.06338881, 0.07015305, 0.07866475, 0.07543724, 0.08013369,\n",
              "        0.07831968, 0.07852816, 0.08654905, 0.08999742]),\n",
              " array([0.0640316 , 0.06624436, 0.06481342, 0.06108964, 0.06338881,\n",
              "        0.07015305, 0.07866475, 0.07543724, 0.08013369, 0.07831968,\n",
              "        0.07852816, 0.08654905, 0.08999742, 0.1101968 ]),\n",
              " array([0.06624436, 0.06481342, 0.06108964, 0.06338881, 0.07015305,\n",
              "        0.07866475, 0.07543724, 0.08013369, 0.07831968, 0.07852816,\n",
              "        0.08654905, 0.08999742, 0.1101968 , 0.14254681]),\n",
              " array([0.06481342, 0.06108964, 0.06338881, 0.07015305, 0.07866475,\n",
              "        0.07543724, 0.08013369, 0.07831968, 0.07852816, 0.08654905,\n",
              "        0.08999742, 0.1101968 , 0.14254681, 0.20028336]),\n",
              " array([0.06108964, 0.06338881, 0.07015305, 0.07866475, 0.07543724,\n",
              "        0.08013369, 0.07831968, 0.07852816, 0.08654905, 0.08999742,\n",
              "        0.1101968 , 0.14254681, 0.20028336, 0.27206755]),\n",
              " array([0.06338881, 0.07015305, 0.07866475, 0.07543724, 0.08013369,\n",
              "        0.07831968, 0.07852816, 0.08654905, 0.08999742, 0.1101968 ,\n",
              "        0.14254681, 0.20028336, 0.27206755, 0.34338042]),\n",
              " array([0.07015305, 0.07866475, 0.07543724, 0.08013369, 0.07831968,\n",
              "        0.07852816, 0.08654905, 0.08999742, 0.1101968 , 0.14254681,\n",
              "        0.20028336, 0.27206755, 0.34338042, 0.33660536]),\n",
              " array([0.07866475, 0.07543724, 0.08013369, 0.07831968, 0.07852816,\n",
              "        0.08654905, 0.08999742, 0.1101968 , 0.14254681, 0.20028336,\n",
              "        0.27206755, 0.34338042, 0.33660536, 0.35600306]),\n",
              " array([0.07543724, 0.08013369, 0.07831968, 0.07852816, 0.08654905,\n",
              "        0.08999742, 0.1101968 , 0.14254681, 0.20028336, 0.27206755,\n",
              "        0.34338042, 0.33660536, 0.35600306, 0.35459101]),\n",
              " array([0.08013369, 0.07831968, 0.07852816, 0.08654905, 0.08999742,\n",
              "        0.1101968 , 0.14254681, 0.20028336, 0.27206755, 0.34338042,\n",
              "        0.33660536, 0.35600306, 0.35459101, 0.33385445]),\n",
              " array([0.07831968, 0.07852816, 0.08654905, 0.08999742, 0.1101968 ,\n",
              "        0.14254681, 0.20028336, 0.27206755, 0.34338042, 0.33660536,\n",
              "        0.35600306, 0.35459101, 0.33385445, 0.35727471]),\n",
              " array([0.07852816, 0.08654905, 0.08999742, 0.1101968 , 0.14254681,\n",
              "        0.20028336, 0.27206755, 0.34338042, 0.33660536, 0.35600306,\n",
              "        0.35459101, 0.33385445, 0.35727471, 0.45602778]),\n",
              " array([0.08654905, 0.08999742, 0.1101968 , 0.14254681, 0.20028336,\n",
              "        0.27206755, 0.34338042, 0.33660536, 0.35600306, 0.35459101,\n",
              "        0.33385445, 0.35727471, 0.45602778, 0.46278023]),\n",
              " array([0.08999742, 0.1101968 , 0.14254681, 0.20028336, 0.27206755,\n",
              "        0.34338042, 0.33660536, 0.35600306, 0.35459101, 0.33385445,\n",
              "        0.35727471, 0.45602778, 0.46278023, 0.45638669]),\n",
              " array([0.1101968 , 0.14254681, 0.20028336, 0.27206755, 0.34338042,\n",
              "        0.33660536, 0.35600306, 0.35459101, 0.33385445, 0.35727471,\n",
              "        0.45602778, 0.46278023, 0.45638669, 0.47071062]),\n",
              " array([0.14254681, 0.20028336, 0.27206755, 0.34338042, 0.33660536,\n",
              "        0.35600306, 0.35459101, 0.33385445, 0.35727471, 0.45602778,\n",
              "        0.46278023, 0.45638669, 0.47071062, 0.45096191]),\n",
              " array([0.20028336, 0.27206755, 0.34338042, 0.33660536, 0.35600306,\n",
              "        0.35459101, 0.33385445, 0.35727471, 0.45602778, 0.46278023,\n",
              "        0.45638669, 0.47071062, 0.45096191, 0.48409773]),\n",
              " array([0.27206755, 0.34338042, 0.33660536, 0.35600306, 0.35459101,\n",
              "        0.33385445, 0.35727471, 0.45602778, 0.46278023, 0.45638669,\n",
              "        0.47071062, 0.45096191, 0.48409773, 0.4711119 ]),\n",
              " array([0.34338042, 0.33660536, 0.35600306, 0.35459101, 0.33385445,\n",
              "        0.35727471, 0.45602778, 0.46278023, 0.45638669, 0.47071062,\n",
              "        0.45096191, 0.48409773, 0.4711119 , 0.47644948]),\n",
              " array([0.33660536, 0.35600306, 0.35459101, 0.33385445, 0.35727471,\n",
              "        0.45602778, 0.46278023, 0.45638669, 0.47071062, 0.45096191,\n",
              "        0.48409773, 0.4711119 , 0.47644948, 0.47350995]),\n",
              " array([0.35600306, 0.35459101, 0.33385445, 0.35727471, 0.45602778,\n",
              "        0.46278023, 0.45638669, 0.47071062, 0.45096191, 0.48409773,\n",
              "        0.4711119 , 0.47644948, 0.47350995, 0.51895793]),\n",
              " array([0.35459101, 0.33385445, 0.35727471, 0.45602778, 0.46278023,\n",
              "        0.45638669, 0.47071062, 0.45096191, 0.48409773, 0.4711119 ,\n",
              "        0.47644948, 0.47350995, 0.51895793, 0.46593691]),\n",
              " array([0.33385445, 0.35727471, 0.45602778, 0.46278023, 0.45638669,\n",
              "        0.47071062, 0.45096191, 0.48409773, 0.4711119 , 0.47644948,\n",
              "        0.47350995, 0.51895793, 0.46593691, 0.47713095]),\n",
              " array([0.35727471, 0.45602778, 0.46278023, 0.45638669, 0.47071062,\n",
              "        0.45096191, 0.48409773, 0.4711119 , 0.47644948, 0.47350995,\n",
              "        0.51895793, 0.46593691, 0.47713095, 0.5119135 ]),\n",
              " array([0.45602778, 0.46278023, 0.45638669, 0.47071062, 0.45096191,\n",
              "        0.48409773, 0.4711119 , 0.47644948, 0.47350995, 0.51895793,\n",
              "        0.46593691, 0.47713095, 0.5119135 , 0.50029792]),\n",
              " array([0.46278023, 0.45638669, 0.47071062, 0.45096191, 0.48409773,\n",
              "        0.4711119 , 0.47644948, 0.47350995, 0.51895793, 0.46593691,\n",
              "        0.47713095, 0.5119135 , 0.50029792, 0.50975978]),\n",
              " array([0.45638669, 0.47071062, 0.45096191, 0.48409773, 0.4711119 ,\n",
              "        0.47644948, 0.47350995, 0.51895793, 0.46593691, 0.47713095,\n",
              "        0.5119135 , 0.50029792, 0.50975978, 0.501901  ]),\n",
              " array([0.47071062, 0.45096191, 0.48409773, 0.4711119 , 0.47644948,\n",
              "        0.47350995, 0.51895793, 0.46593691, 0.47713095, 0.5119135 ,\n",
              "        0.50029792, 0.50975978, 0.501901  , 0.47581964]),\n",
              " array([0.45096191, 0.48409773, 0.4711119 , 0.47644948, 0.47350995,\n",
              "        0.51895793, 0.46593691, 0.47713095, 0.5119135 , 0.50029792,\n",
              "        0.50975978, 0.501901  , 0.47581964, 0.46722961]),\n",
              " array([0.48409773, 0.4711119 , 0.47644948, 0.47350995, 0.51895793,\n",
              "        0.46593691, 0.47713095, 0.5119135 , 0.50029792, 0.50975978,\n",
              "        0.501901  , 0.47581964, 0.46722961, 0.4950296 ]),\n",
              " array([0.4711119 , 0.47644948, 0.47350995, 0.51895793, 0.46593691,\n",
              "        0.47713095, 0.5119135 , 0.50029792, 0.50975978, 0.501901  ,\n",
              "        0.47581964, 0.46722961, 0.4950296 , 0.4795859 ]),\n",
              " array([0.47644948, 0.47350995, 0.51895793, 0.46593691, 0.47713095,\n",
              "        0.5119135 , 0.50029792, 0.50975978, 0.501901  , 0.47581964,\n",
              "        0.46722961, 0.4950296 , 0.4795859 , 0.40482906]),\n",
              " array([0.47350995, 0.51895793, 0.46593691, 0.47713095, 0.5119135 ,\n",
              "        0.50029792, 0.50975978, 0.501901  , 0.47581964, 0.46722961,\n",
              "        0.4950296 , 0.4795859 , 0.40482906, 0.3868778 ]),\n",
              " array([0.51895793, 0.46593691, 0.47713095, 0.5119135 , 0.50029792,\n",
              "        0.50975978, 0.501901  , 0.47581964, 0.46722961, 0.4950296 ,\n",
              "        0.4795859 , 0.40482906, 0.3868778 , 0.40138342]),\n",
              " array([0.46593691, 0.47713095, 0.5119135 , 0.50029792, 0.50975978,\n",
              "        0.501901  , 0.47581964, 0.46722961, 0.4950296 , 0.4795859 ,\n",
              "        0.40482906, 0.3868778 , 0.40138342, 0.39278248]),\n",
              " array([0.47713095, 0.5119135 , 0.50029792, 0.50975978, 0.501901  ,\n",
              "        0.47581964, 0.46722961, 0.4950296 , 0.4795859 , 0.40482906,\n",
              "        0.3868778 , 0.40138342, 0.39278248, 0.39168355]),\n",
              " array([0.5119135 , 0.50029792, 0.50975978, 0.501901  , 0.47581964,\n",
              "        0.46722961, 0.4950296 , 0.4795859 , 0.40482906, 0.3868778 ,\n",
              "        0.40138342, 0.39278248, 0.39168355, 0.41977815]),\n",
              " array([0.50029792, 0.50975978, 0.501901  , 0.47581964, 0.46722961,\n",
              "        0.4950296 , 0.4795859 , 0.40482906, 0.3868778 , 0.40138342,\n",
              "        0.39278248, 0.39168355, 0.41977815, 0.56486174]),\n",
              " array([0.50975978, 0.501901  , 0.47581964, 0.46722961, 0.4950296 ,\n",
              "        0.4795859 , 0.40482906, 0.3868778 , 0.40138342, 0.39278248,\n",
              "        0.39168355, 0.41977815, 0.56486174, 0.52874411]),\n",
              " array([0.501901  , 0.47581964, 0.46722961, 0.4950296 , 0.4795859 ,\n",
              "        0.40482906, 0.3868778 , 0.40138342, 0.39278248, 0.39168355,\n",
              "        0.41977815, 0.56486174, 0.52874411, 0.59215523]),\n",
              " array([0.47581964, 0.46722961, 0.4950296 , 0.4795859 , 0.40482906,\n",
              "        0.3868778 , 0.40138342, 0.39278248, 0.39168355, 0.41977815,\n",
              "        0.56486174, 0.52874411, 0.59215523, 0.59724127]),\n",
              " array([0.46722961, 0.4950296 , 0.4795859 , 0.40482906, 0.3868778 ,\n",
              "        0.40138342, 0.39278248, 0.39168355, 0.41977815, 0.56486174,\n",
              "        0.52874411, 0.59215523, 0.59724127, 0.57714808]),\n",
              " array([0.4950296 , 0.4795859 , 0.40482906, 0.3868778 , 0.40138342,\n",
              "        0.39278248, 0.39168355, 0.41977815, 0.56486174, 0.52874411,\n",
              "        0.59215523, 0.59724127, 0.57714808, 0.60730598]),\n",
              " array([0.4795859 , 0.40482906, 0.3868778 , 0.40138342, 0.39278248,\n",
              "        0.39168355, 0.41977815, 0.56486174, 0.52874411, 0.59215523,\n",
              "        0.59724127, 0.57714808, 0.60730598, 0.59065356]),\n",
              " array([0.40482906, 0.3868778 , 0.40138342, 0.39278248, 0.39168355,\n",
              "        0.41977815, 0.56486174, 0.52874411, 0.59215523, 0.59724127,\n",
              "        0.57714808, 0.60730598, 0.59065356, 0.57548966]),\n",
              " array([0.3868778 , 0.40138342, 0.39278248, 0.39168355, 0.41977815,\n",
              "        0.56486174, 0.52874411, 0.59215523, 0.59724127, 0.57714808,\n",
              "        0.60730598, 0.59065356, 0.57548966, 0.55888565]),\n",
              " array([0.40138342, 0.39278248, 0.39168355, 0.41977815, 0.56486174,\n",
              "        0.52874411, 0.59215523, 0.59724127, 0.57714808, 0.60730598,\n",
              "        0.59065356, 0.57548966, 0.55888565, 0.51899105]),\n",
              " array([0.39278248, 0.39168355, 0.41977815, 0.56486174, 0.52874411,\n",
              "        0.59215523, 0.59724127, 0.57714808, 0.60730598, 0.59065356,\n",
              "        0.57548966, 0.55888565, 0.51899105, 0.51755904]),\n",
              " array([0.39168355, 0.41977815, 0.56486174, 0.52874411, 0.59215523,\n",
              "        0.59724127, 0.57714808, 0.60730598, 0.59065356, 0.57548966,\n",
              "        0.55888565, 0.51899105, 0.51755904, 0.5390327 ]),\n",
              " array([0.41977815, 0.56486174, 0.52874411, 0.59215523, 0.59724127,\n",
              "        0.57714808, 0.60730598, 0.59065356, 0.57548966, 0.55888565,\n",
              "        0.51899105, 0.51755904, 0.5390327 , 0.5339871 ]),\n",
              " array([0.56486174, 0.52874411, 0.59215523, 0.59724127, 0.57714808,\n",
              "        0.60730598, 0.59065356, 0.57548966, 0.55888565, 0.51899105,\n",
              "        0.51755904, 0.5390327 , 0.5339871 , 0.5166458 ]),\n",
              " array([0.52874411, 0.59215523, 0.59724127, 0.57714808, 0.60730598,\n",
              "        0.59065356, 0.57548966, 0.55888565, 0.51899105, 0.51755904,\n",
              "        0.5390327 , 0.5339871 , 0.5166458 , 0.54402902]),\n",
              " array([0.59215523, 0.59724127, 0.57714808, 0.60730598, 0.59065356,\n",
              "        0.57548966, 0.55888565, 0.51899105, 0.51755904, 0.5390327 ,\n",
              "        0.5339871 , 0.5166458 , 0.54402902, 0.53332384]),\n",
              " array([0.59724127, 0.57714808, 0.60730598, 0.59065356, 0.57548966,\n",
              "        0.55888565, 0.51899105, 0.51755904, 0.5390327 , 0.5339871 ,\n",
              "        0.5166458 , 0.54402902, 0.53332384, 0.46026819]),\n",
              " array([0.57714808, 0.60730598, 0.59065356, 0.57548966, 0.55888565,\n",
              "        0.51899105, 0.51755904, 0.5390327 , 0.5339871 , 0.5166458 ,\n",
              "        0.54402902, 0.53332384, 0.46026819, 0.45952291]),\n",
              " array([0.60730598, 0.59065356, 0.57548966, 0.55888565, 0.51899105,\n",
              "        0.51755904, 0.5390327 , 0.5339871 , 0.5166458 , 0.54402902,\n",
              "        0.53332384, 0.46026819, 0.45952291, 0.46365167]),\n",
              " array([0.59065356, 0.57548966, 0.55888565, 0.51899105, 0.51755904,\n",
              "        0.5390327 , 0.5339871 , 0.5166458 , 0.54402902, 0.53332384,\n",
              "        0.46026819, 0.45952291, 0.46365167, 0.46448493]),\n",
              " array([0.57548966, 0.55888565, 0.51899105, 0.51755904, 0.5390327 ,\n",
              "        0.5339871 , 0.5166458 , 0.54402902, 0.53332384, 0.46026819,\n",
              "        0.45952291, 0.46365167, 0.46448493, 0.46507872]),\n",
              " array([0.55888565, 0.51899105, 0.51755904, 0.5390327 , 0.5339871 ,\n",
              "        0.5166458 , 0.54402902, 0.53332384, 0.46026819, 0.45952291,\n",
              "        0.46365167, 0.46448493, 0.46507872, 0.43601506]),\n",
              " array([0.51899105, 0.51755904, 0.5390327 , 0.5339871 , 0.5166458 ,\n",
              "        0.54402902, 0.53332384, 0.46026819, 0.45952291, 0.46365167,\n",
              "        0.46448493, 0.46507872, 0.43601506, 0.34603372]),\n",
              " array([0.51755904, 0.5390327 , 0.5339871 , 0.5166458 , 0.54402902,\n",
              "        0.53332384, 0.46026819, 0.45952291, 0.46365167, 0.46448493,\n",
              "        0.46507872, 0.43601506, 0.34603372, 0.34603372]),\n",
              " array([0.5390327 , 0.5339871 , 0.5166458 , 0.54402902, 0.53332384,\n",
              "        0.46026819, 0.45952291, 0.46365167, 0.46448493, 0.46507872,\n",
              "        0.43601506, 0.34603372, 0.34603372, 0.33654711]),\n",
              " array([0.5339871 , 0.5166458 , 0.54402902, 0.53332384, 0.46026819,\n",
              "        0.45952291, 0.46365167, 0.46448493, 0.46507872, 0.43601506,\n",
              "        0.34603372, 0.34603372, 0.33654711, 0.36742282]),\n",
              " array([0.5166458 , 0.54402902, 0.53332384, 0.46026819, 0.45952291,\n",
              "        0.46365167, 0.46448493, 0.46507872, 0.43601506, 0.34603372,\n",
              "        0.34603372, 0.33654711, 0.36742282, 0.46318716]),\n",
              " array([0.54402902, 0.53332384, 0.46026819, 0.45952291, 0.46365167,\n",
              "        0.46448493, 0.46507872, 0.43601506, 0.34603372, 0.34603372,\n",
              "        0.33654711, 0.36742282, 0.46318716, 0.59958876]),\n",
              " array([0.53332384, 0.46026819, 0.45952291, 0.46365167, 0.46448493,\n",
              "        0.46507872, 0.43601506, 0.34603372, 0.34603372, 0.33654711,\n",
              "        0.36742282, 0.46318716, 0.59958876, 0.63082825]),\n",
              " array([0.46026819, 0.45952291, 0.46365167, 0.46448493, 0.46507872,\n",
              "        0.43601506, 0.34603372, 0.34603372, 0.33654711, 0.36742282,\n",
              "        0.46318716, 0.59958876, 0.63082825, 0.61507465]),\n",
              " array([0.45952291, 0.46365167, 0.46448493, 0.46507872, 0.43601506,\n",
              "        0.34603372, 0.34603372, 0.33654711, 0.36742282, 0.46318716,\n",
              "        0.59958876, 0.63082825, 0.61507465, 0.65560824]),\n",
              " array([0.46365167, 0.46448493, 0.46507872, 0.43601506, 0.34603372,\n",
              "        0.34603372, 0.33654711, 0.36742282, 0.46318716, 0.59958876,\n",
              "        0.63082825, 0.61507465, 0.65560824, 0.82475723]),\n",
              " array([0.46448493, 0.46507872, 0.43601506, 0.34603372, 0.34603372,\n",
              "        0.33654711, 0.36742282, 0.46318716, 0.59958876, 0.63082825,\n",
              "        0.61507465, 0.65560824, 0.82475723, 0.79342928]),\n",
              " array([0.46507872, 0.43601506, 0.34603372, 0.34603372, 0.33654711,\n",
              "        0.36742282, 0.46318716, 0.59958876, 0.63082825, 0.61507465,\n",
              "        0.65560824, 0.82475723, 0.79342928, 0.85906806]),\n",
              " array([0.43601506, 0.34603372, 0.34603372, 0.33654711, 0.36742282,\n",
              "        0.46318716, 0.59958876, 0.63082825, 0.61507465, 0.65560824,\n",
              "        0.82475723, 0.79342928, 0.85906806, 0.83479233]),\n",
              " array([0.34603372, 0.34603372, 0.33654711, 0.36742282, 0.46318716,\n",
              "        0.59958876, 0.63082825, 0.61507465, 0.65560824, 0.82475723,\n",
              "        0.79342928, 0.85906806, 0.83479233, 0.86787856]),\n",
              " array([0.34603372, 0.33654711, 0.36742282, 0.46318716, 0.59958876,\n",
              "        0.63082825, 0.61507465, 0.65560824, 0.82475723, 0.79342928,\n",
              "        0.85906806, 0.83479233, 0.86787856, 0.80996538]),\n",
              " array([0.33654711, 0.36742282, 0.46318716, 0.59958876, 0.63082825,\n",
              "        0.61507465, 0.65560824, 0.82475723, 0.79342928, 0.85906806,\n",
              "        0.83479233, 0.86787856, 0.80996538, 0.87988842]),\n",
              " array([0.36742282, 0.46318716, 0.59958876, 0.63082825, 0.61507465,\n",
              "        0.65560824, 0.82475723, 0.79342928, 0.85906806, 0.83479233,\n",
              "        0.86787856, 0.80996538, 0.87988842, 0.83404647]),\n",
              " array([0.46318716, 0.59958876, 0.63082825, 0.61507465, 0.65560824,\n",
              "        0.82475723, 0.79342928, 0.85906806, 0.83479233, 0.86787856,\n",
              "        0.80996538, 0.87988842, 0.83404647, 0.83414447]),\n",
              " array([0.59958876, 0.63082825, 0.61507465, 0.65560824, 0.82475723,\n",
              "        0.79342928, 0.85906806, 0.83479233, 0.86787856, 0.80996538,\n",
              "        0.87988842, 0.83404647, 0.83414447, 0.85826024]),\n",
              " array([0.63082825, 0.61507465, 0.65560824, 0.82475723, 0.79342928,\n",
              "        0.85906806, 0.83479233, 0.86787856, 0.80996538, 0.87988842,\n",
              "        0.83404647, 0.83414447, 0.85826024, 0.78972898]),\n",
              " array([0.61507465, 0.65560824, 0.82475723, 0.79342928, 0.85906806,\n",
              "        0.83479233, 0.86787856, 0.80996538, 0.87988842, 0.83404647,\n",
              "        0.83414447, 0.85826024, 0.78972898, 0.79002339]),\n",
              " array([0.65560824, 0.82475723, 0.79342928, 0.85906806, 0.83479233,\n",
              "        0.86787856, 0.80996538, 0.87988842, 0.83404647, 0.83414447,\n",
              "        0.85826024, 0.78972898, 0.79002339, 0.85950549]),\n",
              " array([0.82475723, 0.79342928, 0.85906806, 0.83479233, 0.86787856,\n",
              "        0.80996538, 0.87988842, 0.83404647, 0.83414447, 0.85826024,\n",
              "        0.78972898, 0.79002339, 0.85950549, 0.83204112]),\n",
              " array([0.79342928, 0.85906806, 0.83479233, 0.86787856, 0.80996538,\n",
              "        0.87988842, 0.83404647, 0.83414447, 0.85826024, 0.78972898,\n",
              "        0.79002339, 0.85950549, 0.83204112, 0.86281552]),\n",
              " array([0.85906806, 0.83479233, 0.86787856, 0.80996538, 0.87988842,\n",
              "        0.83404647, 0.83414447, 0.85826024, 0.78972898, 0.79002339,\n",
              "        0.85950549, 0.83204112, 0.86281552, 0.85826394]),\n",
              " array([0.83479233, 0.86787856, 0.80996538, 0.87988842, 0.83404647,\n",
              "        0.83414447, 0.85826024, 0.78972898, 0.79002339, 0.85950549,\n",
              "        0.83204112, 0.86281552, 0.85826394, 0.8405726 ]),\n",
              " array([0.86787856, 0.80996538, 0.87988842, 0.83404647, 0.83414447,\n",
              "        0.85826024, 0.78972898, 0.79002339, 0.85950549, 0.83204112,\n",
              "        0.86281552, 0.85826394, 0.8405726 , 0.8037319 ]),\n",
              " array([0.80996538, 0.87988842, 0.83404647, 0.83414447, 0.85826024,\n",
              "        0.78972898, 0.79002339, 0.85950549, 0.83204112, 0.86281552,\n",
              "        0.85826394, 0.8405726 , 0.8037319 , 0.82941422]),\n",
              " array([0.87988842, 0.83404647, 0.83414447, 0.85826024, 0.78972898,\n",
              "        0.79002339, 0.85950549, 0.83204112, 0.86281552, 0.85826394,\n",
              "        0.8405726 , 0.8037319 , 0.82941422, 0.83216485]),\n",
              " array([0.83404647, 0.83414447, 0.85826024, 0.78972898, 0.79002339,\n",
              "        0.85950549, 0.83204112, 0.86281552, 0.85826394, 0.8405726 ,\n",
              "        0.8037319 , 0.82941422, 0.83216485, 0.82891756]),\n",
              " array([0.83414447, 0.85826024, 0.78972898, 0.79002339, 0.85950549,\n",
              "        0.83204112, 0.86281552, 0.85826394, 0.8405726 , 0.8037319 ,\n",
              "        0.82941422, 0.83216485, 0.82891756, 0.8298423 ]),\n",
              " array([0.85826024, 0.78972898, 0.79002339, 0.85950549, 0.83204112,\n",
              "        0.86281552, 0.85826394, 0.8405726 , 0.8037319 , 0.82941422,\n",
              "        0.83216485, 0.82891756, 0.8298423 , 0.83132585]),\n",
              " array([0.78972898, 0.79002339, 0.85950549, 0.83204112, 0.86281552,\n",
              "        0.85826394, 0.8405726 , 0.8037319 , 0.82941422, 0.83216485,\n",
              "        0.82891756, 0.8298423 , 0.83132585, 0.84989243]),\n",
              " array([0.79002339, 0.85950549, 0.83204112, 0.86281552, 0.85826394,\n",
              "        0.8405726 , 0.8037319 , 0.82941422, 0.83216485, 0.82891756,\n",
              "        0.8298423 , 0.83132585, 0.84989243, 0.93842529]),\n",
              " array([0.85950549, 0.83204112, 0.86281552, 0.85826394, 0.8405726 ,\n",
              "        0.8037319 , 0.82941422, 0.83216485, 0.82891756, 0.8298423 ,\n",
              "        0.83132585, 0.84989243, 0.93842529, 0.97159258]),\n",
              " array([0.83204112, 0.86281552, 0.85826394, 0.8405726 , 0.8037319 ,\n",
              "        0.82941422, 0.83216485, 0.82891756, 0.8298423 , 0.83132585,\n",
              "        0.84989243, 0.93842529, 0.97159258, 0.96171881]),\n",
              " array([0.86281552, 0.85826394, 0.8405726 , 0.8037319 , 0.82941422,\n",
              "        0.83216485, 0.82891756, 0.8298423 , 0.83132585, 0.84989243,\n",
              "        0.93842529, 0.97159258, 0.96171881, 0.96409436]),\n",
              " array([0.85826394, 0.8405726 , 0.8037319 , 0.82941422, 0.83216485,\n",
              "        0.82891756, 0.8298423 , 0.83132585, 0.84989243, 0.93842529,\n",
              "        0.97159258, 0.96171881, 0.96409436, 0.99674297]),\n",
              " array([0.8405726 , 0.8037319 , 0.82941422, 0.83216485, 0.82891756,\n",
              "        0.8298423 , 0.83132585, 0.84989243, 0.93842529, 0.97159258,\n",
              "        0.96171881, 0.96409436, 0.99674297, 0.98546827]),\n",
              " array([0.8037319 , 0.82941422, 0.83216485, 0.82891756, 0.8298423 ,\n",
              "        0.83132585, 0.84989243, 0.93842529, 0.97159258, 0.96171881,\n",
              "        0.96409436, 0.99674297, 0.98546827, 0.96224938]),\n",
              " array([0.82941422, 0.83216485, 0.82891756, 0.8298423 , 0.83132585,\n",
              "        0.84989243, 0.93842529, 0.97159258, 0.96171881, 0.96409436,\n",
              "        0.99674297, 0.98546827, 0.96224938, 0.90840865]),\n",
              " array([0.83216485, 0.82891756, 0.8298423 , 0.83132585, 0.84989243,\n",
              "        0.93842529, 0.97159258, 0.96171881, 0.96409436, 0.99674297,\n",
              "        0.98546827, 0.96224938, 0.90840865, 0.97158089]),\n",
              " array([0.82891756, 0.8298423 , 0.83132585, 0.84989243, 0.93842529,\n",
              "        0.97159258, 0.96171881, 0.96409436, 0.99674297, 0.98546827,\n",
              "        0.96224938, 0.90840865, 0.97158089, 0.87759568]),\n",
              " array([0.8298423 , 0.83132585, 0.84989243, 0.93842529, 0.97159258,\n",
              "        0.96171881, 0.96409436, 0.99674297, 0.98546827, 0.96224938,\n",
              "        0.90840865, 0.97158089, 0.87759568, 0.84052428]),\n",
              " array([0.83132585, 0.84989243, 0.93842529, 0.97159258, 0.96171881,\n",
              "        0.96409436, 0.99674297, 0.98546827, 0.96224938, 0.90840865,\n",
              "        0.97158089, 0.87759568, 0.84052428, 0.79839841]),\n",
              " array([0.84989243, 0.93842529, 0.97159258, 0.96171881, 0.96409436,\n",
              "        0.99674297, 0.98546827, 0.96224938, 0.90840865, 0.97158089,\n",
              "        0.87759568, 0.84052428, 0.79839841, 0.83902514]),\n",
              " array([0.93842529, 0.97159258, 0.96171881, 0.96409436, 0.99674297,\n",
              "        0.98546827, 0.96224938, 0.90840865, 0.97158089, 0.87759568,\n",
              "        0.84052428, 0.79839841, 0.83902514, 0.80465469]),\n",
              " array([0.97159258, 0.96171881, 0.96409436, 0.99674297, 0.98546827,\n",
              "        0.96224938, 0.90840865, 0.97158089, 0.87759568, 0.84052428,\n",
              "        0.79839841, 0.83902514, 0.80465469, 0.79921345]),\n",
              " array([0.96171881, 0.96409436, 0.99674297, 0.98546827, 0.96224938,\n",
              "        0.90840865, 0.97158089, 0.87759568, 0.84052428, 0.79839841,\n",
              "        0.83902514, 0.80465469, 0.79921345, 0.86407519]),\n",
              " array([0.96409436, 0.99674297, 0.98546827, 0.96224938, 0.90840865,\n",
              "        0.97158089, 0.87759568, 0.84052428, 0.79839841, 0.83902514,\n",
              "        0.80465469, 0.79921345, 0.86407519, 0.87363253]),\n",
              " array([0.99674297, 0.98546827, 0.96224938, 0.90840865, 0.97158089,\n",
              "        0.87759568, 0.84052428, 0.79839841, 0.83902514, 0.80465469,\n",
              "        0.79921345, 0.86407519, 0.87363253, 0.94055105]),\n",
              " array([0.98546827, 0.96224938, 0.90840865, 0.97158089, 0.87759568,\n",
              "        0.84052428, 0.79839841, 0.83902514, 0.80465469, 0.79921345,\n",
              "        0.86407519, 0.87363253, 0.94055105, 0.93940887]),\n",
              " array([0.96224938, 0.90840865, 0.97158089, 0.87759568, 0.84052428,\n",
              "        0.79839841, 0.83902514, 0.80465469, 0.79921345, 0.86407519,\n",
              "        0.87363253, 0.94055105, 0.93940887, 0.93330418]),\n",
              " array([0.90840865, 0.97158089, 0.87759568, 0.84052428, 0.79839841,\n",
              "        0.83902514, 0.80465469, 0.79921345, 0.86407519, 0.87363253,\n",
              "        0.94055105, 0.93940887, 0.93330418, 0.92091809]),\n",
              " array([0.97158089, 0.87759568, 0.84052428, 0.79839841, 0.83902514,\n",
              "        0.80465469, 0.79921345, 0.86407519, 0.87363253, 0.94055105,\n",
              "        0.93940887, 0.93330418, 0.92091809, 0.84011257]),\n",
              " array([0.87759568, 0.84052428, 0.79839841, 0.83902514, 0.80465469,\n",
              "        0.79921345, 0.86407519, 0.87363253, 0.94055105, 0.93940887,\n",
              "        0.93330418, 0.92091809, 0.84011257, 0.88966164]),\n",
              " array([0.84052428, 0.79839841, 0.83902514, 0.80465469, 0.79921345,\n",
              "        0.86407519, 0.87363253, 0.94055105, 0.93940887, 0.93330418,\n",
              "        0.92091809, 0.84011257, 0.88966164, 0.91337194]),\n",
              " array([0.79839841, 0.83902514, 0.80465469, 0.79921345, 0.86407519,\n",
              "        0.87363253, 0.94055105, 0.93940887, 0.93330418, 0.92091809,\n",
              "        0.84011257, 0.88966164, 0.91337194, 0.88714834]),\n",
              " array([0.83902514, 0.80465469, 0.79921345, 0.86407519, 0.87363253,\n",
              "        0.94055105, 0.93940887, 0.93330418, 0.92091809, 0.84011257,\n",
              "        0.88966164, 0.91337194, 0.88714834, 0.92655495]),\n",
              " array([0.80465469, 0.79921345, 0.86407519, 0.87363253, 0.94055105,\n",
              "        0.93940887, 0.93330418, 0.92091809, 0.84011257, 0.88966164,\n",
              "        0.91337194, 0.88714834, 0.92655495, 0.88421865]),\n",
              " array([0.79921345, 0.86407519, 0.87363253, 0.94055105, 0.93940887,\n",
              "        0.93330418, 0.92091809, 0.84011257, 0.88966164, 0.91337194,\n",
              "        0.88714834, 0.92655495, 0.88421865, 0.84015914]),\n",
              " array([0.86407519, 0.87363253, 0.94055105, 0.93940887, 0.93330418,\n",
              "        0.92091809, 0.84011257, 0.88966164, 0.91337194, 0.88714834,\n",
              "        0.92655495, 0.88421865, 0.84015914, 0.78835903]),\n",
              " array([0.87363253, 0.94055105, 0.93940887, 0.93330418, 0.92091809,\n",
              "        0.84011257, 0.88966164, 0.91337194, 0.88714834, 0.92655495,\n",
              "        0.88421865, 0.84015914, 0.78835903, 0.78639967]),\n",
              " array([0.94055105, 0.93940887, 0.93330418, 0.92091809, 0.84011257,\n",
              "        0.88966164, 0.91337194, 0.88714834, 0.92655495, 0.88421865,\n",
              "        0.84015914, 0.78835903, 0.78639967, 0.78354402]),\n",
              " array([0.93940887, 0.93330418, 0.92091809, 0.84011257, 0.88966164,\n",
              "        0.91337194, 0.88714834, 0.92655495, 0.88421865, 0.84015914,\n",
              "        0.78835903, 0.78639967, 0.78354402, 0.75869564]),\n",
              " array([0.93330418, 0.92091809, 0.84011257, 0.88966164, 0.91337194,\n",
              "        0.88714834, 0.92655495, 0.88421865, 0.84015914, 0.78835903,\n",
              "        0.78639967, 0.78354402, 0.75869564, 0.76713496]),\n",
              " array([0.92091809, 0.84011257, 0.88966164, 0.91337194, 0.88714834,\n",
              "        0.92655495, 0.88421865, 0.84015914, 0.78835903, 0.78639967,\n",
              "        0.78354402, 0.75869564, 0.76713496, 0.76584411]),\n",
              " array([0.84011257, 0.88966164, 0.91337194, 0.88714834, 0.92655495,\n",
              "        0.88421865, 0.84015914, 0.78835903, 0.78639967, 0.78354402,\n",
              "        0.75869564, 0.76713496, 0.76584411, 0.76208691]),\n",
              " array([0.88966164, 0.91337194, 0.88714834, 0.92655495, 0.88421865,\n",
              "        0.84015914, 0.78835903, 0.78639967, 0.78354402, 0.75869564,\n",
              "        0.76713496, 0.76584411, 0.76208691, 0.76766045]),\n",
              " array([0.91337194, 0.88714834, 0.92655495, 0.88421865, 0.84015914,\n",
              "        0.78835903, 0.78639967, 0.78354402, 0.75869564, 0.76713496,\n",
              "        0.76584411, 0.76208691, 0.76766045, 0.75866212]),\n",
              " array([0.88714834, 0.92655495, 0.88421865, 0.84015914, 0.78835903,\n",
              "        0.78639967, 0.78354402, 0.75869564, 0.76713496, 0.76584411,\n",
              "        0.76208691, 0.76766045, 0.75866212, 0.78158446]),\n",
              " array([0.92655495, 0.88421865, 0.84015914, 0.78835903, 0.78639967,\n",
              "        0.78354402, 0.75869564, 0.76713496, 0.76584411, 0.76208691,\n",
              "        0.76766045, 0.75866212, 0.78158446, 0.74767794]),\n",
              " array([0.88421865, 0.84015914, 0.78835903, 0.78639967, 0.78354402,\n",
              "        0.75869564, 0.76713496, 0.76584411, 0.76208691, 0.76766045,\n",
              "        0.75866212, 0.78158446, 0.74767794, 0.75120619]),\n",
              " array([0.84015914, 0.78835903, 0.78639967, 0.78354402, 0.75869564,\n",
              "        0.76713496, 0.76584411, 0.76208691, 0.76766045, 0.75866212,\n",
              "        0.78158446, 0.74767794, 0.75120619, 0.76746074]),\n",
              " array([0.78835903, 0.78639967, 0.78354402, 0.75869564, 0.76713496,\n",
              "        0.76584411, 0.76208691, 0.76766045, 0.75866212, 0.78158446,\n",
              "        0.74767794, 0.75120619, 0.76746074, 0.74128488]),\n",
              " array([0.78639967, 0.78354402, 0.75869564, 0.76713496, 0.76584411,\n",
              "        0.76208691, 0.76766045, 0.75866212, 0.78158446, 0.74767794,\n",
              "        0.75120619, 0.76746074, 0.74128488, 0.74966633]),\n",
              " array([0.78354402, 0.75869564, 0.76713496, 0.76584411, 0.76208691,\n",
              "        0.76766045, 0.75866212, 0.78158446, 0.74767794, 0.75120619,\n",
              "        0.76746074, 0.74128488, 0.74966633, 0.77452387]),\n",
              " array([0.75869564, 0.76713496, 0.76584411, 0.76208691, 0.76766045,\n",
              "        0.75866212, 0.78158446, 0.74767794, 0.75120619, 0.76746074,\n",
              "        0.74128488, 0.74966633, 0.77452387, 0.72626194]),\n",
              " array([0.76713496, 0.76584411, 0.76208691, 0.76766045, 0.75866212,\n",
              "        0.78158446, 0.74767794, 0.75120619, 0.76746074, 0.74128488,\n",
              "        0.74966633, 0.77452387, 0.72626194, 0.74298724]),\n",
              " array([0.76584411, 0.76208691, 0.76766045, 0.75866212, 0.78158446,\n",
              "        0.74767794, 0.75120619, 0.76746074, 0.74128488, 0.74966633,\n",
              "        0.77452387, 0.72626194, 0.74298724, 0.75539263]),\n",
              " array([0.76208691, 0.76766045, 0.75866212, 0.78158446, 0.74767794,\n",
              "        0.75120619, 0.76746074, 0.74128488, 0.74966633, 0.77452387,\n",
              "        0.72626194, 0.74298724, 0.75539263, 0.76551443]),\n",
              " array([0.76766045, 0.75866212, 0.78158446, 0.74767794, 0.75120619,\n",
              "        0.76746074, 0.74128488, 0.74966633, 0.77452387, 0.72626194,\n",
              "        0.74298724, 0.75539263, 0.76551443, 0.8476332 ]),\n",
              " array([0.75866212, 0.78158446, 0.74767794, 0.75120619, 0.76746074,\n",
              "        0.74128488, 0.74966633, 0.77452387, 0.72626194, 0.74298724,\n",
              "        0.75539263, 0.76551443, 0.8476332 , 0.82392934]),\n",
              " array([0.78158446, 0.74767794, 0.75120619, 0.76746074, 0.74128488,\n",
              "        0.74966633, 0.77452387, 0.72626194, 0.74298724, 0.75539263,\n",
              "        0.76551443, 0.8476332 , 0.82392934, 0.8204346 ]),\n",
              " array([0.74767794, 0.75120619, 0.76746074, 0.74128488, 0.74966633,\n",
              "        0.77452387, 0.72626194, 0.74298724, 0.75539263, 0.76551443,\n",
              "        0.8476332 , 0.82392934, 0.8204346 , 0.78306256]),\n",
              " array([0.75120619, 0.76746074, 0.74128488, 0.74966633, 0.77452387,\n",
              "        0.72626194, 0.74298724, 0.75539263, 0.76551443, 0.8476332 ,\n",
              "        0.82392934, 0.8204346 , 0.78306256, 0.73974112]),\n",
              " array([0.76746074, 0.74128488, 0.74966633, 0.77452387, 0.72626194,\n",
              "        0.74298724, 0.75539263, 0.76551443, 0.8476332 , 0.82392934,\n",
              "        0.8204346 , 0.78306256, 0.73974112, 0.76629673]),\n",
              " array([0.74128488, 0.74966633, 0.77452387, 0.72626194, 0.74298724,\n",
              "        0.75539263, 0.76551443, 0.8476332 , 0.82392934, 0.8204346 ,\n",
              "        0.78306256, 0.73974112, 0.76629673, 0.75434105]),\n",
              " array([0.74966633, 0.77452387, 0.72626194, 0.74298724, 0.75539263,\n",
              "        0.76551443, 0.8476332 , 0.82392934, 0.8204346 , 0.78306256,\n",
              "        0.73974112, 0.76629673, 0.75434105, 0.75765575]),\n",
              " array([0.77452387, 0.72626194, 0.74298724, 0.75539263, 0.76551443,\n",
              "        0.8476332 , 0.82392934, 0.8204346 , 0.78306256, 0.73974112,\n",
              "        0.76629673, 0.75434105, 0.75765575, 0.66917491]),\n",
              " array([0.72626194, 0.74298724, 0.75539263, 0.76551443, 0.8476332 ,\n",
              "        0.82392934, 0.8204346 , 0.78306256, 0.73974112, 0.76629673,\n",
              "        0.75434105, 0.75765575, 0.66917491, 0.7615092 ]),\n",
              " array([0.74298724, 0.75539263, 0.76551443, 0.8476332 , 0.82392934,\n",
              "        0.8204346 , 0.78306256, 0.73974112, 0.76629673, 0.75434105,\n",
              "        0.75765575, 0.66917491, 0.7615092 , 0.69828201]),\n",
              " array([0.75539263, 0.76551443, 0.8476332 , 0.82392934, 0.8204346 ,\n",
              "        0.78306256, 0.73974112, 0.76629673, 0.75434105, 0.75765575,\n",
              "        0.66917491, 0.7615092 , 0.69828201, 0.70794457]),\n",
              " array([0.76551443, 0.8476332 , 0.82392934, 0.8204346 , 0.78306256,\n",
              "        0.73974112, 0.76629673, 0.75434105, 0.75765575, 0.66917491,\n",
              "        0.7615092 , 0.69828201, 0.70794457, 0.70689202]),\n",
              " array([0.8476332 , 0.82392934, 0.8204346 , 0.78306256, 0.73974112,\n",
              "        0.76629673, 0.75434105, 0.75765575, 0.66917491, 0.7615092 ,\n",
              "        0.69828201, 0.70794457, 0.70689202, 0.66493878]),\n",
              " array([0.82392934, 0.8204346 , 0.78306256, 0.73974112, 0.76629673,\n",
              "        0.75434105, 0.75765575, 0.66917491, 0.7615092 , 0.69828201,\n",
              "        0.70794457, 0.70689202, 0.66493878, 0.66680968]),\n",
              " array([0.8204346 , 0.78306256, 0.73974112, 0.76629673, 0.75434105,\n",
              "        0.75765575, 0.66917491, 0.7615092 , 0.69828201, 0.70794457,\n",
              "        0.70689202, 0.66493878, 0.66680968, 0.6678297 ]),\n",
              " array([0.78306256, 0.73974112, 0.76629673, 0.75434105, 0.75765575,\n",
              "        0.66917491, 0.7615092 , 0.69828201, 0.70794457, 0.70689202,\n",
              "        0.66493878, 0.66680968, 0.6678297 , 0.66245782]),\n",
              " array([0.73974112, 0.76629673, 0.75434105, 0.75765575, 0.66917491,\n",
              "        0.7615092 , 0.69828201, 0.70794457, 0.70689202, 0.66493878,\n",
              "        0.66680968, 0.6678297 , 0.66245782, 0.61134259]),\n",
              " array([0.76629673, 0.75434105, 0.75765575, 0.66917491, 0.7615092 ,\n",
              "        0.69828201, 0.70794457, 0.70689202, 0.66493878, 0.66680968,\n",
              "        0.6678297 , 0.66245782, 0.61134259, 0.62804216]),\n",
              " array([0.75434105, 0.75765575, 0.66917491, 0.7615092 , 0.69828201,\n",
              "        0.70794457, 0.70689202, 0.66493878, 0.66680968, 0.6678297 ,\n",
              "        0.66245782, 0.61134259, 0.62804216, 0.67445832]),\n",
              " array([0.75765575, 0.66917491, 0.7615092 , 0.69828201, 0.70794457,\n",
              "        0.70689202, 0.66493878, 0.66680968, 0.6678297 , 0.66245782,\n",
              "        0.61134259, 0.62804216, 0.67445832, 0.68652098]),\n",
              " array([0.66917491, 0.7615092 , 0.69828201, 0.70794457, 0.70689202,\n",
              "        0.66493878, 0.66680968, 0.6678297 , 0.66245782, 0.61134259,\n",
              "        0.62804216, 0.67445832, 0.68652098, 0.6751658 ]),\n",
              " array([0.7615092 , 0.69828201, 0.70794457, 0.70689202, 0.66493878,\n",
              "        0.66680968, 0.6678297 , 0.66245782, 0.61134259, 0.62804216,\n",
              "        0.67445832, 0.68652098, 0.6751658 , 0.710423  ]),\n",
              " array([0.69828201, 0.70794457, 0.70689202, 0.66493878, 0.66680968,\n",
              "        0.6678297 , 0.66245782, 0.61134259, 0.62804216, 0.67445832,\n",
              "        0.68652098, 0.6751658 , 0.710423  , 0.80979158]),\n",
              " array([0.70794457, 0.70689202, 0.66493878, 0.66680968, 0.6678297 ,\n",
              "        0.66245782, 0.61134259, 0.62804216, 0.67445832, 0.68652098,\n",
              "        0.6751658 , 0.710423  , 0.80979158, 0.91229016]),\n",
              " array([0.70689202, 0.66493878, 0.66680968, 0.6678297 , 0.66245782,\n",
              "        0.61134259, 0.62804216, 0.67445832, 0.68652098, 0.6751658 ,\n",
              "        0.710423  , 0.80979158, 0.91229016, 0.90269716]),\n",
              " array([0.66493878, 0.66680968, 0.6678297 , 0.66245782, 0.61134259,\n",
              "        0.62804216, 0.67445832, 0.68652098, 0.6751658 , 0.710423  ,\n",
              "        0.80979158, 0.91229016, 0.90269716, 0.96325848]),\n",
              " array([0.66680968, 0.6678297 , 0.66245782, 0.61134259, 0.62804216,\n",
              "        0.67445832, 0.68652098, 0.6751658 , 0.710423  , 0.80979158,\n",
              "        0.91229016, 0.90269716, 0.96325848, 0.9535891 ]),\n",
              " array([0.6678297 , 0.66245782, 0.61134259, 0.62804216, 0.67445832,\n",
              "        0.68652098, 0.6751658 , 0.710423  , 0.80979158, 0.91229016,\n",
              "        0.90269716, 0.96325848, 0.9535891 , 0.9427226 ]),\n",
              " array([0.66245782, 0.61134259, 0.62804216, 0.67445832, 0.68652098,\n",
              "        0.6751658 , 0.710423  , 0.80979158, 0.91229016, 0.90269716,\n",
              "        0.96325848, 0.9535891 , 0.9427226 , 0.92694289]),\n",
              " array([0.61134259, 0.62804216, 0.67445832, 0.68652098, 0.6751658 ,\n",
              "        0.710423  , 0.80979158, 0.91229016, 0.90269716, 0.96325848,\n",
              "        0.9535891 , 0.9427226 , 0.92694289, 0.96124339]),\n",
              " array([0.62804216, 0.67445832, 0.68652098, 0.6751658 , 0.710423  ,\n",
              "        0.80979158, 0.91229016, 0.90269716, 0.96325848, 0.9535891 ,\n",
              "        0.9427226 , 0.92694289, 0.96124339, 0.93001618]),\n",
              " array([0.67445832, 0.68652098, 0.6751658 , 0.710423  , 0.80979158,\n",
              "        0.91229016, 0.90269716, 0.96325848, 0.9535891 , 0.9427226 ,\n",
              "        0.92694289, 0.96124339, 0.93001618, 0.95347044])]"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWqvJCtTLOfE",
        "outputId": "756e4069-da4b-45c1-d648-aa69ba1831e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "277"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#convert train data to numpy arrays\n",
        "x_train,y_train=np.array(x_train),np.array(y_train)\n",
        "\n",
        "#Reshape\n",
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anKxAcCc5lM8",
        "outputId": "6053eee7-f0e9-4eb8-b201-bb2a9ba92dd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(277,)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# x_train=np.reshape(x_train,(x_train.shape[0],x_train.shape[1],1))\n",
        "# x_train.shape"
      ],
      "metadata": {
        "id": "dkxYCbNG5mq0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# build LSTM model\n",
        "model=Sequential()\n",
        "model.add(LSTM(50,return_sequences=True,input_shape=(x_train.shape[1], 1)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(50,return_sequences=False))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "L3h3sql_5n_5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bbf8545-7fff-4274-b614-40a5bd8e5f2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 14, 50)            10400     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 14, 50)            0         \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 50)                20200     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 25)                1275      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 25)                650       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 32551 (127.15 KB)\n",
            "Trainable params: 32551 (127.15 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#compile the model\n",
        "model.compile(optimizer='adam',loss='mean_squared_error')\n"
      ],
      "metadata": {
        "id": "E_cUyLpv5o_M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train the model\n",
        "model.fit(x_train,y_train,batch_size=7,epochs=4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oRwLkb_P5q-x",
        "outputId": "142cab36-e461-43f2-d2ed-56482158dd59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "40/40 [==============================] - 6s 15ms/step - loss: 0.0630\n",
            "Epoch 2/4\n",
            "40/40 [==============================] - 1s 15ms/step - loss: 0.0179\n",
            "Epoch 3/4\n",
            "40/40 [==============================] - 1s 15ms/step - loss: 0.0123\n",
            "Epoch 4/4\n",
            "40/40 [==============================] - 1s 15ms/step - loss: 0.0129\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7b54621299c0>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create the testing data set\n",
        "#create a new array containing scaled values from index 1543 to 2003\n",
        "test_data=scaled_data[training_data_len - numDays:,:]\n",
        "#create the data sets x_test and y_test\n",
        "x_test=[]\n",
        "y_test=dataset[training_data_len:,:]\n",
        "for i in range(numDays,len(test_data)):\n",
        "  x_test.append(test_data[i-numDays:i,0])\n",
        "\n",
        "#convert the data to a numpy array\n",
        "x_test=np.array(x_test)\n",
        "#reshape\n",
        "x_test=np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))\n",
        "\n"
      ],
      "metadata": {
        "id": "4GBbo6Ly5r_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=model.predict(x_test)\n",
        "predictions=scaler.inverse_transform(predictions)\n"
      ],
      "metadata": {
        "id": "zotOGemb52B8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6417245-de86-4b57-f46e-f93044155c1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 1s 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train=data[:training_data_len]\n",
        "valid=data[training_data_len:]\n",
        "valid['Predictions']=predictions\n",
        "\n",
        "#visualize the data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.title('Model')\n",
        "plt.xlabel('Date',fontsize=18)\n",
        "plt.ylabel('Close Price USD($)',fontsize=18)\n",
        "plt.plot(train['Close'])\n",
        "plt.plot(valid[['Close','Predictions']])\n",
        "plt.legend(['Train','Val','Predictions'],loc='upper right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 810
        },
        "id": "___ujzgi53HK",
        "outputId": "3e68fbb8-e125-49eb-ade9-ca4727a603e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-101-f93ced420f5d>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  valid['Predictions']=predictions\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}