01_data_extractor_alpaca.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-30T14:45:06.187853Z",
     "start_time": "2023-11-30T14:45:02.208249500Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\plotly_resampler\\aggregation\\aggregators.py:25: UserWarning: Could not import lttbc; will use a (slower) python alternative.\n",
      "  warnings.warn(\"Could not import lttbc; will use a (slower) python alternative.\")\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "from alpaca.data.historical import CryptoHistoricalDataClient\n",
    "from alpaca.data.requests import CryptoBarsRequest\n",
    "from alpaca.data.timeframe import TimeFrame\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import neuralprophet\n",
    "import numpy as np\n",
    "import os\n",
    "import plotly.express as px\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "9f58a37e61594a5da0594c4a563231c3"
      },
      "text/plain": "FigureWidgetResampler({\n    'data': [{'hovertemplate': 'variable=close<br>timestamp=%{x}<br>value=%{y}<extra></extra>',\n              'legendgroup': 'close',\n              'line': {'color': '#636efa', 'dash': 'solid'},\n              'marker': {'symbol': 'circle'},\n              'mode': 'lines',\n              'name': '<b style=\"color:sandybrown\">[R]</b> close <i style=\"color:#fc9944\">~4h</i>',\n              'showlegend': True,\n              'type': 'scattergl',\n              'uid': 'cc784c96-7686-4ab9-9ecf-89448fc917a0',\n              'x': array([datetime.datetime(2023, 7, 1, 0, 0, tzinfo=TzInfo(UTC)),\n                          datetime.datetime(2023, 7, 1, 3, 0, tzinfo=TzInfo(UTC)),\n                          datetime.datetime(2023, 7, 1, 6, 0, tzinfo=TzInfo(UTC)), ...,\n                          datetime.datetime(2023, 11, 30, 8, 0, tzinfo=TzInfo(UTC)),\n                          datetime.datetime(2023, 11, 30, 12, 0, tzinfo=TzInfo(UTC)),\n                          datetime.datetime(2023, 11, 30, 14, 0, tzinfo=TzInfo(UTC))],\n                         dtype=object),\n              'xaxis': 'x',\n              'y': array([30466.189525  , 30384.93538301, 30444.52448674, ..., 37676.475     ,\n                          37909.44      , 37630.282     ]),\n              'yaxis': 'y'}],\n    'layout': {'legend': {'title': {'text': 'variable'}, 'tracegroupgap': 0},\n               'margin': {'t': 60},\n               'template': '...',\n               'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'timestamp'}},\n               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'value'}}}\n})"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no keys required for crypto data\n",
    "client = CryptoHistoricalDataClient()\n",
    "\n",
    "symbol = \"BTC/USD\"\n",
    "request_params = CryptoBarsRequest(\n",
    "    # symbol_or_symbols=[\"BTC/USD\", \"ETH/USD\"],\n",
    "    symbol_or_symbols=symbol,\n",
    "    timeframe=TimeFrame.Hour,\n",
    "    start=datetime.strptime(\"2023-07-01\", '%Y-%m-%d')\n",
    ")\n",
    "\n",
    "bars = client.get_crypto_bars(request_params)\n",
    "\n",
    "# convert to dataframe\n",
    "btc = bars.df.loc[symbol].close\n",
    "\n",
    "px.line(btc)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T14:45:08.579298100Z",
     "start_time": "2023-11-30T14:45:06.188851400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.972% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
      "INFO - (NP.utils.set_auto_seasonalities) - Disabling yearly seasonality. Run NeuralProphet with yearly_seasonality=True to override this.\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 32\n",
      "INFO - (NP.utils.configure_trainer) - Using accelerator cpu with 1 device(s).\n",
      "WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (112) is too small than the required number for the learning rate finder (239). The results might not be optimal.\n"
     ]
    },
    {
     "data": {
      "text/plain": "Finding best initial lr:   0%|          | 0/239 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "265fe72c76204a1b87ee6e7abbfb060d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Training: 0it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "42686c1011654eb59638d7d53f53c2f5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.972% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.973% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.973% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
     ]
    },
    {
     "data": {
      "text/plain": "Predicting: 112it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "697bf3f534894093a2c4798061dcb566"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "series = btc\n",
    "lookback_period = 90\n",
    "# Set index to datetime\n",
    "series.index = pd.to_datetime(series.index)\n",
    "series.index = series.index.tz_localize(None)\n",
    "\n",
    "# sort by index\n",
    "series.sort_index(inplace=True)\n",
    "\n",
    "# Trim leading 0s, negative values, and np.nan values\n",
    "first_good_idx = series.dropna()[series > 0].index[0]\n",
    "series = series.loc[first_good_idx:]\n",
    "\n",
    "# Convert series to correct format for Prophet model\n",
    "ds = series.index\n",
    "y = series.values.reshape(-1)\n",
    "df_preprocessed = pd.DataFrame({\"ds\": ds, \"y\": y})\n",
    "\n",
    "# Split df_preprocessed into train dataframe and lookback period dataframe\n",
    "train_df, val_df = (\n",
    "    df_preprocessed.iloc[: -lookback_period],\n",
    "    df_preprocessed.iloc[-lookback_period :],\n",
    ")\n",
    "\n",
    "pi = 0.996  # prediction interval\n",
    "qts = [\n",
    "    (1 - pi) / 2,\n",
    "    pi + (1 - pi) / 2,\n",
    "]  # quantiles based on the prediction interval\n",
    "# Default neural prophet params in a dict\n",
    "neural_prophet_params = dict(\n",
    "    quantiles=qts,\n",
    "    loss_func=\"Huber\",\n",
    "    daily_seasonality=False,\n",
    "    weekly_seasonality=\"auto\",\n",
    "    yearly_seasonality=\"auto\",\n",
    "    seasonality_mode=\"additive\",\n",
    "    normalize=\"auto\",\n",
    "    n_changepoints=25,\n",
    "    n_lags=0,\n",
    "    accelerator=\"cpu\",\n",
    ")\n",
    "\n",
    "neural_prophet_params[\"n_forecasts\"] = (\n",
    "    lookback_period if neural_prophet_params.get(\"n_lags\", 0) > 0 else 1\n",
    ")\n",
    "\n",
    "model = neuralprophet.NeuralProphet(**neural_prophet_params)\n",
    "\n",
    "model.fit(\n",
    "    train_df,\n",
    "    freq='H',\n",
    "    early_stopping=True,\n",
    "    progress=\"bar\",\n",
    "    epochs=300,\n",
    ")\n",
    "\n",
    "future = model.make_future_dataframe(\n",
    "    train_df,\n",
    "    periods=lookback_period,\n",
    "    n_historic_predictions=len(train_df),\n",
    ")\n",
    "\n",
    "prophet_forecast = model.predict(future)\n",
    "\n",
    "# index of column name that starts with \"yhat\"\n",
    "yhat_col_index = prophet_forecast.columns.str.startswith(\"yhat\")\n",
    "# replace string yhat with yhat1\n",
    "# get all column names into a array\n",
    "col_names = prophet_forecast.columns[yhat_col_index].values\n",
    "yhatnames = [re.match(r\"yhat(\\d+)\\s?\", x).group(1) for x in col_names]\n",
    "maxYhatName = \"yhat\" + min(yhatnames)\n",
    "maxYhatNameRegexp = re.compile(maxYhatName + \"($|\\s)\")\n",
    "yhat_col_index_re = prophet_forecast.filter(regex=maxYhatNameRegexp).columns\n",
    "# yhat_col_index = prophet_forecast.columns.str.startswith(maxYhatName)\n",
    "\n",
    "prophet_forecast.rename(\n",
    "    columns={yhat_col_index_re[0]: \"yhat\"},\n",
    "    inplace=True,\n",
    ")\n",
    "prophet_forecast.rename(\n",
    "    columns={yhat_col_index_re[1]: \"yhat_lower\"},\n",
    "    inplace=True,\n",
    ")\n",
    "prophet_forecast.rename(\n",
    "    columns={yhat_col_index_re[2]: \"yhat_upper\"},\n",
    "    inplace=True,\n",
    ")\n",
    "fig, ax = plt.subplots(figize=plt.figaspect(1/3))\n",
    "prophet_forecast.plot(x='ds', y=['y', 'yhat'], ax=ax)\n",
    "\n",
    "train_forecast, val_forecast = (\n",
    "    prophet_forecast.iloc[: -lookback_period],\n",
    "    prophet_forecast.iloc[-lookback_period :],\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T14:45:32.216017100Z",
     "start_time": "2023-11-30T14:45:08.581298800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x17d96c9db70>]"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1440x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot train/lookback dataframes for ground truth and forecasts\n",
    "fig, ax = plt.subplots(figsize=plt.figaspect(1 / 3), facecolor=\"w\")\n",
    "ax.plot(\n",
    "    train_df.ds,\n",
    "    train_df.y,\n",
    "    c=\"tab:blue\",\n",
    "    label=\"Ground Truth (Training)\",\n",
    ")\n",
    "ax.plot(\n",
    "    val_df.ds,\n",
    "    val_df.y,\n",
    "    \"b:\",\n",
    "    label=\"Ground Truth (Lookback Period)\",\n",
    ")\n",
    "ax.plot(\n",
    "    train_forecast.ds,\n",
    "    train_forecast.yhat,\n",
    "    \"r\",\n",
    "    label=\"Forecast (Training)\",\n",
    ")\n",
    "ax.plot(\n",
    "    val_forecast.ds,\n",
    "    val_forecast.yhat,\n",
    "    \"r:\",\n",
    "    label=\"Forecast (Lookback Period)\",\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T14:48:59.297561Z",
     "start_time": "2023-11-30T14:48:59.118215400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "(654.1488416817849, 1280.4094988255226)"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_rmse = mean_squared_error(train_df.y, train_forecast.yhat, squared=False)\n",
    "val_rmse = mean_squared_error(val_df.y, val_forecast.yhat, squared=False)\n",
    "\n",
    "train_rmse, val_rmse"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T14:45:32.268520500Z",
     "start_time": "2023-11-30T14:45:32.212057800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/plain": "True"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "torch.cuda.is_available()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T14:40:47.666583700Z",
     "start_time": "2023-11-30T14:40:45.083639800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "You requested to find -1 devices but there are no visible CUDA devices on this machine.",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[60], line 5\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mlightning\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpytorch\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01maccelerators\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m find_usable_cuda_devices\n\u001B[0;32m      4\u001B[0m \u001B[38;5;66;03m# Find two GPUs on the system that are not already occupied\u001B[39;00m\n\u001B[1;32m----> 5\u001B[0m trainer \u001B[38;5;241m=\u001B[39m Trainer(accelerator\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcuda\u001B[39m\u001B[38;5;124m\"\u001B[39m, devices\u001B[38;5;241m=\u001B[39m\u001B[43mfind_usable_cuda_devices\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m)\n",
      "File \u001B[1;32m~\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\bitcoin-price-predictor-Frh5AYeV-py3.10\\lib\\site-packages\\lightning\\fabric\\accelerators\\cuda.py:97\u001B[0m, in \u001B[0;36mfind_usable_cuda_devices\u001B[1;34m(num_devices)\u001B[0m\n\u001B[0;32m     95\u001B[0m visible_devices \u001B[38;5;241m=\u001B[39m _get_all_visible_cuda_devices()\n\u001B[0;32m     96\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m visible_devices:\n\u001B[1;32m---> 97\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m     98\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mYou requested to find \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnum_devices\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m devices but there are no visible CUDA devices on this machine.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m     99\u001B[0m     )\n\u001B[0;32m    100\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m num_devices \u001B[38;5;241m>\u001B[39m \u001B[38;5;28mlen\u001B[39m(visible_devices):\n\u001B[0;32m    101\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m    102\u001B[0m         \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mYou requested to find \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mnum_devices\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m devices but this machine only has \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(visible_devices)\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m GPUs.\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    103\u001B[0m     )\n",
      "\u001B[1;31mValueError\u001B[0m: You requested to find -1 devices but there are no visible CUDA devices on this machine."
     ]
    }
   ],
   "source": [
    "from lightning import Trainer\n",
    "from lightning.pytorch.accelerators import find_usable_cuda_devices\n",
    "\n",
    "# Find two GPUs on the system that are not already occupied\n",
    "trainer = Trainer(accelerator=\"cuda\", devices=find_usable_cuda_devices())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T13:43:31.874568900Z",
     "start_time": "2023-11-30T13:43:31.837560900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "data": {
      "text/plain": "                      ds             y          yhat    yhat_lower  \\\n0    2023-07-01 00:00:00  30466.189525  30174.335938  28398.130859   \n1    2023-07-01 01:00:00  30437.826899  30177.582031  28199.582031   \n2    2023-07-01 02:00:00  30425.985000  30181.009766  27985.728516   \n3    2023-07-01 03:00:00  30384.935383  30184.533203  27758.609375   \n4    2023-07-01 04:00:00  30388.474456  30188.074219  27520.462891   \n...                  ...           ...           ...           ...   \n3657 2023-11-30 09:00:00           NaN  39808.394531  36420.925781   \n3658 2023-11-30 10:00:00           NaN  39810.437500  36326.527344   \n3659 2023-11-30 11:00:00           NaN  39811.500000  36247.890625   \n3660 2023-11-30 12:00:00           NaN  39811.632812  36186.117188   \n3661 2023-11-30 13:00:00           NaN  39810.898438  36142.062500   \n\n        yhat_upper         trend  season_weekly  \n0     32036.404297  30311.398438    -137.062210  \n1     32063.542969  30310.986328    -133.403580  \n2     32088.535156  30310.574219    -129.565506  \n3     32110.748047  30310.164062    -125.630371  \n4     32129.607422  30309.753906    -121.678696  \n...            ...           ...            ...  \n3657  46915.234375  39657.515625     150.875473  \n3658  46827.859375  39666.734375     143.702667  \n3659  46732.671875  39675.953125     135.545349  \n3660  46630.664062  39685.171875     126.457947  \n3661  46522.875000  39694.390625     116.504562  \n\n[3662 rows x 7 columns]",
      "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>ds</th>\n      <th>y</th>\n      <th>yhat</th>\n      <th>yhat_lower</th>\n      <th>yhat_upper</th>\n      <th>trend</th>\n      <th>season_weekly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-07-01 00:00:00</td>\n      <td>30466.189525</td>\n      <td>30174.335938</td>\n      <td>28398.130859</td>\n      <td>32036.404297</td>\n      <td>30311.398438</td>\n      <td>-137.062210</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-07-01 01:00:00</td>\n      <td>30437.826899</td>\n      <td>30177.582031</td>\n      <td>28199.582031</td>\n      <td>32063.542969</td>\n      <td>30310.986328</td>\n      <td>-133.403580</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-07-01 02:00:00</td>\n      <td>30425.985000</td>\n      <td>30181.009766</td>\n      <td>27985.728516</td>\n      <td>32088.535156</td>\n      <td>30310.574219</td>\n      <td>-129.565506</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-07-01 03:00:00</td>\n      <td>30384.935383</td>\n      <td>30184.533203</td>\n      <td>27758.609375</td>\n      <td>32110.748047</td>\n      <td>30310.164062</td>\n      <td>-125.630371</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-07-01 04:00:00</td>\n      <td>30388.474456</td>\n      <td>30188.074219</td>\n      <td>27520.462891</td>\n      <td>32129.607422</td>\n      <td>30309.753906</td>\n      <td>-121.678696</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3657</th>\n      <td>2023-11-30 09:00:00</td>\n      <td>NaN</td>\n      <td>39808.394531</td>\n      <td>36420.925781</td>\n      <td>46915.234375</td>\n      <td>39657.515625</td>\n      <td>150.875473</td>\n    </tr>\n    <tr>\n      <th>3658</th>\n      <td>2023-11-30 10:00:00</td>\n      <td>NaN</td>\n      <td>39810.437500</td>\n      <td>36326.527344</td>\n      <td>46827.859375</td>\n      <td>39666.734375</td>\n      <td>143.702667</td>\n    </tr>\n    <tr>\n      <th>3659</th>\n      <td>2023-11-30 11:00:00</td>\n      <td>NaN</td>\n      <td>39811.500000</td>\n      <td>36247.890625</td>\n      <td>46732.671875</td>\n      <td>39675.953125</td>\n      <td>135.545349</td>\n    </tr>\n    <tr>\n      <th>3660</th>\n      <td>2023-11-30 12:00:00</td>\n      <td>NaN</td>\n      <td>39811.632812</td>\n      <td>36186.117188</td>\n      <td>46630.664062</td>\n      <td>39685.171875</td>\n      <td>126.457947</td>\n    </tr>\n    <tr>\n      <th>3661</th>\n      <td>2023-11-30 13:00:00</td>\n      <td>NaN</td>\n      <td>39810.898438</td>\n      <td>36142.062500</td>\n      <td>46522.875000</td>\n      <td>39694.390625</td>\n      <td>116.504562</td>\n    </tr>\n  </tbody>\n</table>\n<p>3662 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prophet_forecast"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T13:36:49.256925300Z",
     "start_time": "2023-11-30T13:36:49.238823400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.9% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Dataframe freq automatically defined as H\n",
      "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
      "INFO - (NP.utils.set_auto_seasonalities) - Disabling yearly seasonality. Run NeuralProphet with yearly_seasonality=True to override this.\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 32\n",
      "WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (32) is too small than the required number for the learning rate finder (226). The results might not be optimal.\n"
     ]
    },
    {
     "data": {
      "text/plain": "Finding best initial lr:   0%|          | 0/226 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7e571b42ba104bfbbeb8f78b1d074aa9"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Training: 0it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d3f67a222e564833b06f56b6e3c80b63"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.9% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.908% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.908% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
     ]
    },
    {
     "data": {
      "text/plain": "Predicting: 32it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "acb6e6b5cae6466f8f4a6b33fe75598b"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
    {
     "data": {
      "text/plain": "                      ds         y     yhat1     trend  season_weekly  \\\n0    2020-01-01 00:00:00  0.506144  0.177362  0.157337       0.001543   \n1    2020-01-01 01:00:00  0.871015  0.153688  0.159027       0.001846   \n2    2020-01-01 02:00:00  0.946954  0.150470  0.160717       0.002033   \n3    2020-01-01 03:00:00  0.856699  0.166129  0.162407       0.002102   \n4    2020-01-01 04:00:00  0.087336  0.165808  0.164097       0.002049   \n...                  ...       ...       ...       ...            ...   \n1085 2020-02-15 05:00:00       NaN  0.364616  0.388224       0.000857   \n1086 2020-02-15 06:00:00       NaN  0.355281  0.387836       0.000419   \n1087 2020-02-15 07:00:00       NaN  0.376032  0.387448       0.000041   \n1088 2020-02-15 08:00:00       NaN  0.384731  0.387060      -0.000268   \n1089 2020-02-15 09:00:00       NaN  0.353278  0.386672      -0.000497   \n\n      season_daily  \n0         0.018482  \n1        -0.007184  \n2        -0.012280  \n3         0.001621  \n4        -0.000338  \n...            ...  \n1085     -0.024466  \n1086     -0.032974  \n1087     -0.011457  \n1088     -0.002060  \n1089     -0.032896  \n\n[1090 rows x 6 columns]",
      "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>ds</th>\n      <th>y</th>\n      <th>yhat1</th>\n      <th>trend</th>\n      <th>season_weekly</th>\n      <th>season_daily</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2020-01-01 00:00:00</td>\n      <td>0.506144</td>\n      <td>0.177362</td>\n      <td>0.157337</td>\n      <td>0.001543</td>\n      <td>0.018482</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2020-01-01 01:00:00</td>\n      <td>0.871015</td>\n      <td>0.153688</td>\n      <td>0.159027</td>\n      <td>0.001846</td>\n      <td>-0.007184</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2020-01-01 02:00:00</td>\n      <td>0.946954</td>\n      <td>0.150470</td>\n      <td>0.160717</td>\n      <td>0.002033</td>\n      <td>-0.012280</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2020-01-01 03:00:00</td>\n      <td>0.856699</td>\n      <td>0.166129</td>\n      <td>0.162407</td>\n      <td>0.002102</td>\n      <td>0.001621</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2020-01-01 04:00:00</td>\n      <td>0.087336</td>\n      <td>0.165808</td>\n      <td>0.164097</td>\n      <td>0.002049</td>\n      <td>-0.000338</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1085</th>\n      <td>2020-02-15 05:00:00</td>\n      <td>NaN</td>\n      <td>0.364616</td>\n      <td>0.388224</td>\n      <td>0.000857</td>\n      <td>-0.024466</td>\n    </tr>\n    <tr>\n      <th>1086</th>\n      <td>2020-02-15 06:00:00</td>\n      <td>NaN</td>\n      <td>0.355281</td>\n      <td>0.387836</td>\n      <td>0.000419</td>\n      <td>-0.032974</td>\n    </tr>\n    <tr>\n      <th>1087</th>\n      <td>2020-02-15 07:00:00</td>\n      <td>NaN</td>\n      <td>0.376032</td>\n      <td>0.387448</td>\n      <td>0.000041</td>\n      <td>-0.011457</td>\n    </tr>\n    <tr>\n      <th>1088</th>\n      <td>2020-02-15 08:00:00</td>\n      <td>NaN</td>\n      <td>0.384731</td>\n      <td>0.387060</td>\n      <td>-0.000268</td>\n      <td>-0.002060</td>\n    </tr>\n    <tr>\n      <th>1089</th>\n      <td>2020-02-15 09:00:00</td>\n      <td>NaN</td>\n      <td>0.353278</td>\n      <td>0.386672</td>\n      <td>-0.000497</td>\n      <td>-0.032896</td>\n    </tr>\n  </tbody>\n</table>\n<p>1090 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_range = pd.date_range(start='2020-01-01', periods=1000, freq='H')\n",
    "\n",
    "# Generate 1000 random numbers\n",
    "random_numbers = np.random.rand(1000)\n",
    "\n",
    "# Create a DataFrame with 'ds' as datetime and 'y' as random numbers\n",
    "train_df = pd.DataFrame({'ds': date_range, 'y': random_numbers})\n",
    "\n",
    "model = neuralprophet.NeuralProphet()\n",
    "\n",
    "model.fit(\n",
    "    train_df,\n",
    "    epochs=10,\n",
    ")\n",
    "\n",
    "future = model.make_future_dataframe(\n",
    "    train_df,\n",
    "    periods=90,\n",
    "    n_historic_predictions=len(train_df),\n",
    ")\n",
    "\n",
    "p = model.predict(future, decompose=True, raw=False)\n",
    "p"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T13:33:29.600459100Z",
     "start_time": "2023-11-30T13:33:27.893052600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.908% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.908% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
     ]
    },
    {
     "data": {
      "text/plain": "Predicting: 32it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "c6b7c4d4843b46759778027dde9f92a4"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
    {
     "data": {
      "text/plain": "                      ds         y     yhat1\n0    2020-01-01 00:00:00  0.506144  0.177362\n1    2020-01-01 01:00:00  0.871015  0.153688\n2    2020-01-01 02:00:00  0.946954  0.150470\n3    2020-01-01 03:00:00  0.856699  0.166129\n4    2020-01-01 04:00:00  0.087336  0.165808\n...                  ...       ...       ...\n1085 2020-02-15 05:00:00       NaN  0.364616\n1086 2020-02-15 06:00:00       NaN  0.355281\n1087 2020-02-15 07:00:00       NaN  0.376032\n1088 2020-02-15 08:00:00       NaN  0.384731\n1089 2020-02-15 09:00:00       NaN  0.353278\n\n[1090 rows x 3 columns]",
      "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>ds</th>\n      <th>y</th>\n      <th>yhat1</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2020-01-01 00:00:00</td>\n      <td>0.506144</td>\n      <td>0.177362</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2020-01-01 01:00:00</td>\n      <td>0.871015</td>\n      <td>0.153688</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2020-01-01 02:00:00</td>\n      <td>0.946954</td>\n      <td>0.150470</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2020-01-01 03:00:00</td>\n      <td>0.856699</td>\n      <td>0.166129</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2020-01-01 04:00:00</td>\n      <td>0.087336</td>\n      <td>0.165808</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1085</th>\n      <td>2020-02-15 05:00:00</td>\n      <td>NaN</td>\n      <td>0.364616</td>\n    </tr>\n    <tr>\n      <th>1086</th>\n      <td>2020-02-15 06:00:00</td>\n      <td>NaN</td>\n      <td>0.355281</td>\n    </tr>\n    <tr>\n      <th>1087</th>\n      <td>2020-02-15 07:00:00</td>\n      <td>NaN</td>\n      <td>0.376032</td>\n    </tr>\n    <tr>\n      <th>1088</th>\n      <td>2020-02-15 08:00:00</td>\n      <td>NaN</td>\n      <td>0.384731</td>\n    </tr>\n    <tr>\n      <th>1089</th>\n      <td>2020-02-15 09:00:00</td>\n      <td>NaN</td>\n      <td>0.353278</td>\n    </tr>\n  </tbody>\n</table>\n<p>1090 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(future, decompose=False, raw=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T13:33:38.101019600Z",
     "start_time": "2023-11-30T13:33:37.990959500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.972% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.972% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
     ]
    },
    {
     "data": {
      "text/plain": "Predicting: 112it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b08722b99f9344729aed750392df3688"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
    {
     "data": {
      "text/plain": "                      ds             y         yhat1    yhat1 0.2%  \\\n0    2023-07-01 00:00:00  30466.189525  30463.400391  29095.285156   \n1    2023-07-01 01:00:00  30437.826899  30467.207031  29183.250000   \n2    2023-07-01 02:00:00  30425.985000  30471.222656  29272.595703   \n3    2023-07-01 03:00:00  30384.935383  30475.353516  29362.367188   \n4    2023-07-01 04:00:00  30388.474456  30479.509766  29451.619141   \n...                  ...           ...           ...           ...   \n3567 2023-11-26 15:00:00  37453.195000  38616.835938  37470.078125   \n3568 2023-11-26 16:00:00  37245.615000  38626.234375  37477.894531   \n3569 2023-11-26 17:00:00  37287.255500  38636.535156  37491.921875   \n3570 2023-11-26 18:00:00  37261.784000  38647.753906  37512.351562   \n3571 2023-11-26 19:00:00  37400.330000  38659.898438  37539.281250   \n\n       yhat1 99.8%         trend  season_weekly  \n0     33192.960938  30577.613281    -114.213417  \n1     33188.839844  30577.353516    -110.144753  \n2     33185.257812  30577.091797    -105.869095  \n3     33182.148438  30576.830078    -101.477211  \n4     33179.441406  30576.570312     -97.059303  \n...            ...           ...            ...  \n3567  40441.085938  38764.250000    -147.414291  \n3568  40459.570312  38772.542969    -146.306870  \n3569  40479.187500  38780.835938    -144.298630  \n3570  40499.953125  38789.125000    -141.371384  \n3571  40521.878906  38797.417969    -137.517517  \n\n[3572 rows x 7 columns]",
      "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>ds</th>\n      <th>y</th>\n      <th>yhat1</th>\n      <th>yhat1 0.2%</th>\n      <th>yhat1 99.8%</th>\n      <th>trend</th>\n      <th>season_weekly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-07-01 00:00:00</td>\n      <td>30466.189525</td>\n      <td>30463.400391</td>\n      <td>29095.285156</td>\n      <td>33192.960938</td>\n      <td>30577.613281</td>\n      <td>-114.213417</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-07-01 01:00:00</td>\n      <td>30437.826899</td>\n      <td>30467.207031</td>\n      <td>29183.250000</td>\n      <td>33188.839844</td>\n      <td>30577.353516</td>\n      <td>-110.144753</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-07-01 02:00:00</td>\n      <td>30425.985000</td>\n      <td>30471.222656</td>\n      <td>29272.595703</td>\n      <td>33185.257812</td>\n      <td>30577.091797</td>\n      <td>-105.869095</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-07-01 03:00:00</td>\n      <td>30384.935383</td>\n      <td>30475.353516</td>\n      <td>29362.367188</td>\n      <td>33182.148438</td>\n      <td>30576.830078</td>\n      <td>-101.477211</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-07-01 04:00:00</td>\n      <td>30388.474456</td>\n      <td>30479.509766</td>\n      <td>29451.619141</td>\n      <td>33179.441406</td>\n      <td>30576.570312</td>\n      <td>-97.059303</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3567</th>\n      <td>2023-11-26 15:00:00</td>\n      <td>37453.195000</td>\n      <td>38616.835938</td>\n      <td>37470.078125</td>\n      <td>40441.085938</td>\n      <td>38764.250000</td>\n      <td>-147.414291</td>\n    </tr>\n    <tr>\n      <th>3568</th>\n      <td>2023-11-26 16:00:00</td>\n      <td>37245.615000</td>\n      <td>38626.234375</td>\n      <td>37477.894531</td>\n      <td>40459.570312</td>\n      <td>38772.542969</td>\n      <td>-146.306870</td>\n    </tr>\n    <tr>\n      <th>3569</th>\n      <td>2023-11-26 17:00:00</td>\n      <td>37287.255500</td>\n      <td>38636.535156</td>\n      <td>37491.921875</td>\n      <td>40479.187500</td>\n      <td>38780.835938</td>\n      <td>-144.298630</td>\n    </tr>\n    <tr>\n      <th>3570</th>\n      <td>2023-11-26 18:00:00</td>\n      <td>37261.784000</td>\n      <td>38647.753906</td>\n      <td>37512.351562</td>\n      <td>40499.953125</td>\n      <td>38789.125000</td>\n      <td>-141.371384</td>\n    </tr>\n    <tr>\n      <th>3571</th>\n      <td>2023-11-26 19:00:00</td>\n      <td>37400.330000</td>\n      <td>38659.898438</td>\n      <td>37539.281250</td>\n      <td>40521.878906</td>\n      <td>38797.417969</td>\n      <td>-137.517517</td>\n    </tr>\n  </tbody>\n</table>\n<p>3572 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(train_df)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T13:17:58.312098400Z",
     "start_time": "2023-11-30T13:17:58.052028400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.972% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.973% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 99.973% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
     ]
    },
    {
     "data": {
      "text/plain": "Predicting: 112it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "5ddd5891e81941cfa48d6fe493f980c0"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
    {
     "data": {
      "text/plain": "                      ds             y         yhat1    yhat1 0.2%  \\\n0    2023-07-01 00:00:00  30466.189525  30463.400391  29095.285156   \n1    2023-07-01 01:00:00  30437.826899  30467.207031  29183.250000   \n2    2023-07-01 02:00:00  30425.985000  30471.222656  29272.595703   \n3    2023-07-01 03:00:00  30384.935383  30475.353516  29362.367188   \n4    2023-07-01 04:00:00  30388.474456  30479.509766  29451.619141   \n...                  ...           ...           ...           ...   \n3657 2023-11-30 09:00:00           NaN  39664.144531  39179.507812   \n3658 2023-11-30 10:00:00           NaN  39666.871094  39133.671875   \n3659 2023-11-30 11:00:00           NaN  39668.585938  39080.160156   \n3660 2023-11-30 12:00:00           NaN  39669.343750  39019.468750   \n3661 2023-11-30 13:00:00           NaN  39669.191406  38952.187500   \n\n       yhat1 99.8%  \n0     33192.960938  \n1     33188.839844  \n2     33185.257812  \n3     33182.148438  \n4     33179.441406  \n...            ...  \n3657  41707.125000  \n3658  41700.156250  \n3659  41692.460938  \n3660  41684.070312  \n3661  41675.023438  \n\n[3662 rows x 5 columns]",
      "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>ds</th>\n      <th>y</th>\n      <th>yhat1</th>\n      <th>yhat1 0.2%</th>\n      <th>yhat1 99.8%</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-07-01 00:00:00</td>\n      <td>30466.189525</td>\n      <td>30463.400391</td>\n      <td>29095.285156</td>\n      <td>33192.960938</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-07-01 01:00:00</td>\n      <td>30437.826899</td>\n      <td>30467.207031</td>\n      <td>29183.250000</td>\n      <td>33188.839844</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-07-01 02:00:00</td>\n      <td>30425.985000</td>\n      <td>30471.222656</td>\n      <td>29272.595703</td>\n      <td>33185.257812</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-07-01 03:00:00</td>\n      <td>30384.935383</td>\n      <td>30475.353516</td>\n      <td>29362.367188</td>\n      <td>33182.148438</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-07-01 04:00:00</td>\n      <td>30388.474456</td>\n      <td>30479.509766</td>\n      <td>29451.619141</td>\n      <td>33179.441406</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3657</th>\n      <td>2023-11-30 09:00:00</td>\n      <td>NaN</td>\n      <td>39664.144531</td>\n      <td>39179.507812</td>\n      <td>41707.125000</td>\n    </tr>\n    <tr>\n      <th>3658</th>\n      <td>2023-11-30 10:00:00</td>\n      <td>NaN</td>\n      <td>39666.871094</td>\n      <td>39133.671875</td>\n      <td>41700.156250</td>\n    </tr>\n    <tr>\n      <th>3659</th>\n      <td>2023-11-30 11:00:00</td>\n      <td>NaN</td>\n      <td>39668.585938</td>\n      <td>39080.160156</td>\n      <td>41692.460938</td>\n    </tr>\n    <tr>\n      <th>3660</th>\n      <td>2023-11-30 12:00:00</td>\n      <td>NaN</td>\n      <td>39669.343750</td>\n      <td>39019.468750</td>\n      <td>41684.070312</td>\n    </tr>\n    <tr>\n      <th>3661</th>\n      <td>2023-11-30 13:00:00</td>\n      <td>NaN</td>\n      <td>39669.191406</td>\n      <td>38952.187500</td>\n      <td>41675.023438</td>\n    </tr>\n  </tbody>\n</table>\n<p>3662 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = model.make_future_dataframe(\n",
    "    train_df,\n",
    "    periods=lookback_period,\n",
    "    n_historic_predictions=len(train_df),\n",
    ")\n",
    "model.predict(future, decompose=False, raw=False)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T13:22:09.602158100Z",
     "start_time": "2023-11-30T13:22:09.354996200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 98.889% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency H corresponds to 98.889% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - H\n"
     ]
    },
    {
     "data": {
      "text/plain": "Predicting: 112it [00:00, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "273fc58cd396430c9cfed26da70dda79"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
    {
     "data": {
      "text/plain": "                    ds           y         yhat1    yhat1 0.2%   yhat1 99.8%  \\\n0  2023-11-26 20:00:00  37600.1000  38672.968750  37572.730469  40544.964844   \n1  2023-11-26 21:00:00  37622.5345  38686.945312  37612.609375  40569.203125   \n2  2023-11-26 22:00:00  37674.5400  38701.812500  37658.750000  40594.570312   \n3  2023-11-26 23:00:00  37460.7450  38717.523438  37710.882812  40621.042969   \n4  2023-11-27 00:00:00  37500.8500  38734.039062  37768.660156  40648.578125   \n..                 ...         ...           ...           ...           ...   \n85 2023-11-30 09:00:00  37704.9400  39664.144531  39179.507812  41707.125000   \n86 2023-11-30 10:00:00  37689.8300  39666.871094  39133.671875  41700.156250   \n87 2023-11-30 11:00:00  37897.1800  39668.585938  39080.160156  41692.460938   \n88 2023-11-30 12:00:00  37909.4400  39669.343750  39019.468750  41684.070312   \n89 2023-11-30 13:00:00  37808.5660  39669.191406  38952.187500  41675.023438   \n\n           trend  season_weekly  \n0   38805.710938    -132.740219  \n1   38814.000000    -127.053551  \n2   38822.292969    -120.482277  \n3   38830.585938    -113.061691  \n4   38838.875000    -104.837250  \n..           ...            ...  \n85  39510.539062     153.605713  \n86  39518.832031     148.036682  \n87  39527.125000     141.464233  \n88  39535.414062     133.929092  \n89  39543.710938     125.482552  \n\n[90 rows x 7 columns]",
      "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>ds</th>\n      <th>y</th>\n      <th>yhat1</th>\n      <th>yhat1 0.2%</th>\n      <th>yhat1 99.8%</th>\n      <th>trend</th>\n      <th>season_weekly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-11-26 20:00:00</td>\n      <td>37600.1000</td>\n      <td>38672.968750</td>\n      <td>37572.730469</td>\n      <td>40544.964844</td>\n      <td>38805.710938</td>\n      <td>-132.740219</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-11-26 21:00:00</td>\n      <td>37622.5345</td>\n      <td>38686.945312</td>\n      <td>37612.609375</td>\n      <td>40569.203125</td>\n      <td>38814.000000</td>\n      <td>-127.053551</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-11-26 22:00:00</td>\n      <td>37674.5400</td>\n      <td>38701.812500</td>\n      <td>37658.750000</td>\n      <td>40594.570312</td>\n      <td>38822.292969</td>\n      <td>-120.482277</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-11-26 23:00:00</td>\n      <td>37460.7450</td>\n      <td>38717.523438</td>\n      <td>37710.882812</td>\n      <td>40621.042969</td>\n      <td>38830.585938</td>\n      <td>-113.061691</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-11-27 00:00:00</td>\n      <td>37500.8500</td>\n      <td>38734.039062</td>\n      <td>37768.660156</td>\n      <td>40648.578125</td>\n      <td>38838.875000</td>\n      <td>-104.837250</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>2023-11-30 09:00:00</td>\n      <td>37704.9400</td>\n      <td>39664.144531</td>\n      <td>39179.507812</td>\n      <td>41707.125000</td>\n      <td>39510.539062</td>\n      <td>153.605713</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>2023-11-30 10:00:00</td>\n      <td>37689.8300</td>\n      <td>39666.871094</td>\n      <td>39133.671875</td>\n      <td>41700.156250</td>\n      <td>39518.832031</td>\n      <td>148.036682</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>2023-11-30 11:00:00</td>\n      <td>37897.1800</td>\n      <td>39668.585938</td>\n      <td>39080.160156</td>\n      <td>41692.460938</td>\n      <td>39527.125000</td>\n      <td>141.464233</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>2023-11-30 12:00:00</td>\n      <td>37909.4400</td>\n      <td>39669.343750</td>\n      <td>39019.468750</td>\n      <td>41684.070312</td>\n      <td>39535.414062</td>\n      <td>133.929092</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>2023-11-30 13:00:00</td>\n      <td>37808.5660</td>\n      <td>39669.191406</td>\n      <td>38952.187500</td>\n      <td>41675.023438</td>\n      <td>39543.710938</td>\n      <td>125.482552</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(val_df)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-30T13:19:30.182165300Z",
     "start_time": "2023-11-30T13:19:30.090594100Z"
    }
   }
  }
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