Preprocessing and Modelling (1) (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Preprocessing and Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Necessary Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d3b4d5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For data manipulation \n",
    "import pandas as pd\n",
    "# For data analysis\n",
    "import numpy as np\n",
    "#For data visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Warning libraries\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Modelling libraries\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import pmdarima as pm#a library to help with auto_arima\n",
    "import itertools\n",
    "\n",
    "# Metrics Libraries\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "000b0cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the cleaned data\n",
    "\n",
    "new_data = pd.read_csv('new_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "15984f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Date</th>\n",
       "      <th>RegionID</th>\n",
       "      <th>Zipcode</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Metro</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>22yr_ROI</th>\n",
       "      <th>22yr_CV</th>\n",
       "      <th>5yr_ROI</th>\n",
       "      <th>5yr_CV</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>84654</td>\n",
       "      <td>60657</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Cook</td>\n",
       "      <td>1</td>\n",
       "      <td>2.083782</td>\n",
       "      <td>0.256487</td>\n",
       "      <td>0.260827</td>\n",
       "      <td>0.066302</td>\n",
       "      <td>334200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>90668</td>\n",
       "      <td>75070</td>\n",
       "      <td>McKinney</td>\n",
       "      <td>TX</td>\n",
       "      <td>Dallas-Fort Worth</td>\n",
       "      <td>Collin</td>\n",
       "      <td>2</td>\n",
       "      <td>0.365295</td>\n",
       "      <td>0.152680</td>\n",
       "      <td>0.533842</td>\n",
       "      <td>0.124597</td>\n",
       "      <td>235700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>91982</td>\n",
       "      <td>77494</td>\n",
       "      <td>Katy</td>\n",
       "      <td>TX</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Harris</td>\n",
       "      <td>3</td>\n",
       "      <td>0.567966</td>\n",
       "      <td>0.143950</td>\n",
       "      <td>0.283158</td>\n",
       "      <td>0.072385</td>\n",
       "      <td>210400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>84616</td>\n",
       "      <td>60614</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>IL</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Cook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.623971</td>\n",
       "      <td>0.237364</td>\n",
       "      <td>0.258788</td>\n",
       "      <td>0.056243</td>\n",
       "      <td>498100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1996-04-01</td>\n",
       "      <td>93144</td>\n",
       "      <td>79936</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>TX</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>5</td>\n",
       "      <td>0.571798</td>\n",
       "      <td>0.178326</td>\n",
       "      <td>0.070485</td>\n",
       "      <td>0.023086</td>\n",
       "      <td>77300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  RegionID  Zipcode      City State              Metro  \\\n",
       "0  1996-04-01     84654    60657   Chicago    IL            Chicago   \n",
       "1  1996-04-01     90668    75070  McKinney    TX  Dallas-Fort Worth   \n",
       "2  1996-04-01     91982    77494      Katy    TX            Houston   \n",
       "3  1996-04-01     84616    60614   Chicago    IL            Chicago   \n",
       "4  1996-04-01     93144    79936   El Paso    TX            El Paso   \n",
       "\n",
       "  CountyName  SizeRank  22yr_ROI   22yr_CV   5yr_ROI    5yr_CV     value  \n",
       "0       Cook         1  2.083782  0.256487  0.260827  0.066302  334200.0  \n",
       "1     Collin         2  0.365295  0.152680  0.533842  0.124597  235700.0  \n",
       "2     Harris         3  0.567966  0.143950  0.283158  0.072385  210400.0  \n",
       "3       Cook         4  1.623971  0.237364  0.258788  0.056243  498100.0  \n",
       "4    El Paso         5  0.571798  0.178326  0.070485  0.023086   77300.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4873e1fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data['Date'] = pd.to_datetime(new_data.Date)\n",
    "new_data.set_index('Date',inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac3b317",
   "metadata": {},
   "source": [
    "## 5.1 Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f8b490",
   "metadata": {},
   "source": [
    "In this phase we will transform the raw data into a form that will make it suitable for building and training our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fadc71fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing our data\n",
    "\n",
    "new_data.value.plot(figsize=(15,10),color = 'maroon');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e20624",
   "metadata": {},
   "source": [
    "From the graph, there seems to be an increasing trend in data. However,let's reconfirm this using both rolling statistics and the Dickey-Fuller test."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173cd8ef",
   "metadata": {},
   "source": [
    "#### Stationarity Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6b484fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a stationarity function \n",
    "def stationarity_check(TS):\n",
    "    \n",
    "    # Import adfuller\n",
    "    from statsmodels.tsa.stattools import adfuller\n",
    "    \n",
    "    # Calculate rolling statistics\n",
    "    roll_mean = TS.rolling(window=6, center=False).mean()\n",
    "    roll_std = TS.rolling(window=6, center=False).std()\n",
    "    \n",
    "    # Perform the Dickey Fuller test\n",
    "    dftest = adfuller(TS) \n",
    "    \n",
    "    # Plot rolling statistics:\n",
    "    fig = plt.figure(figsize=(12,6))\n",
    "    orig = plt.plot(TS, color='blue',label='Original')\n",
    "    mean = plt.plot(roll_mean, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(roll_std, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    # Print Dickey-Fuller test results\n",
    "    print('Results of Dickey-Fuller Test: \\n')\n",
    "\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic', 'p-value', \n",
    "                                             '#Lags Used', 'Number of Observations Used'])\n",
    "    for key, value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)\n",
    "    \n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e117657",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtgAAAF1CAYAAADSuZ2oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAAsTAAALEwEAmpwYAABoM0lEQVR4nO3dd3gU1ffH8fdNgJDQm7TQBelFUAQLKApiQxDBCiiKvTfsvX3t2EUR5KciomABRcWGjSrSkRZ6CxBaAmn398eZxNADbLIpn9fzzJPNzOzs3Z1k9+ydc8913ntERERERCQ0IsLdABERERGRgkQBtoiIiIhICCnAFhEREREJIQXYIiIiIiIhpABbRERERCSEFGCLiIiIiISQAmwRyfOcc3HOudOD24845/4vuF3TObfdORcZ3hbKvjjnajvnvHOuSIiO19E5tzIUxzqMxz7ZObfgCO7/lnPuwVC2SUTyLgXYIpIrgiA5KQiI1zrnhjrnSh7JMb33y733Jb33aaFqZ4agfd45122P9S8F6/uF+jGz0SbnnHvBObcxWEZl4z5NnHPfOec2OecSnHPTnHNnBdvCFrDmhOC87Aj+xjY65yY453qH4tje+4ne+2Oy2Y5+zrnf9rj/td77x0PRFhHJ+xRgi0huOtd7XxJoCbQC7g1vcw7qX6BPxi9BT2wvYHGY2tMZuAxoAVQD3s7Gfb4CvgeqAEcBNwNbc6qBoXIEvd4tgr+xY4ChwGvOuYdD1jARkWxQgC0iuc57vxYYjwXaADjnznPOzQl6WX92zjU62HH2TEEI7ve4c+5359y2oOe2Ypb9+zjnlgW9mw9mTT3Zj6+Ak5xz5YLfzwRmAmv3aMeVzrl5zrnNzrnxzrlaWba94pxb4ZzbGvQen5xl2yPOuZHOuQ+C9s5xzrU5QHtSgCRgrfd+l/f++4O8PhWBOsBg731ysPzuvf/NOVcC+AaoFvT4bnfOVXPOHe+c+zM4D2ucc68554plOaZ3zl3rnFsY7PO6c84F2yKdc8875+Kdc0uAs/dozxXB67TNObfEOXdNlm0dnXMrnXP3OOfWAu8756KDKwmbnXNzgeMO9Hyz8t7He++HA9cB9zrnKgSPU8Y5917w3FY5554I2h0VPJ+mWdpUKbjqctSevf3OuYHOucXBc5nrnOserG8EvAW0C17ThGD9UOfcE1nuf7VzblFwZeFL51y17LzGIpI/KMAWkVznnIsFugKLgt8bAB8DtwKVgHHAV1kDu0NwCXAF1ltbDLgzeIzGwBvApUBVoAxQ/SDH2gl8AVwU/N4H+GCP59INuA/oEbR9YvBcMkzBvkiUBz4CPnXOFc+y/TxgBFAW+BJ47QDtmR8c513nXHbevzdir/H/OefOd85Vztjgvd+BnYPVQZpNSe/9aiANuA2oCLQDOgHX73Hcc7BgtznWo98lWH91sK0V0Aboucf91gfbS2Pn6CXn3LFZtlcJnl8tYADwMFAvWLoAfbPxnPf0BVAEOD74fSiQChwdtLMzcJX3fhfwOXBxlvv2An7x3q/fx3EXAydjf0ePYq9xVe/9POBa4M/gNS275x2dc6cBTwfHrwosw/4Gstrfaywi+YACbBHJTWOcc9uAFViwlXHpvjcw1nv/vfc+BXgeiAbaH8ZjvO+9/9d7nwSM5L9e8p7AV97737z3ycBDgM/G8T4A+jjnygIdgDF7bL8WeNp7P897nwo8BbTM6MX23v+f936j9z7Ve/8CEIWlL2T4zXs/LsgjH46lf+zFOVcU6/W/HihHliDbOfebc+7cPe/jvffAqUAc8AKwxjn3q3Ou/v6erPd+mvf+r6C9cVgaSoc9dnvGe5/gvV8O/MR/r3Ev4GXv/Qrv/SYsiMx67LHe+8Xe/AJ8hwWpGdKBh4Pe+aTgeE967zd571cAg/bX7gM8nxQgHigffME4C7jVe78jCJxf4r8vUB9luQ32Ze2j/Rz3U+/9au99uvf+E2Ah/wXxB3MpMMR7Pz0I7O/FerxrZ9lnf6+xiOQDCrBFJDed770vBXQEGmK9pGD5xMsydvLep2NB+MF6mPcla/pGIpAxkLJacMyMx0jEengPyHv/G9YzfT/wdRD4ZVULeCW4lJ8AbAJcRtudc3cGaRFbgu1l+O9576u9xd2+849PA4p57/8P+0JSBwuyS2Ov5W/7uA/e+5Xe+xu99/WCtu5gj174rJxzDZxzXzsbiLoV+8JQcY/dsvUak+WcBsfu6pz7K0iLSMCC3azH3uC935nl9wMeLzuCLyaVsPNSCyiKfdHIOF9vY1c7wALZGOdc2yDYbQmM3s9x+zjnZmQ5TlP2fp32Z8+/9+3Y32LWv/f9vcYikg8owBaRXBf0Xg7FeqoBVmPBD2DVMoAawKoQPuwaIDbLY0QDFbJ53/8D7mDfgekK4BrvfdksS7T3/g9n+dZ3Yz2x5YJ0gS1YAH6oimDBIUEQeh6WPjAFGOG933ywAwS9wK9jwSDsuwf/TSwVpb73vjSW/pLd9q7BzluGmhk3nHNRwGfYOa8cvBbj9jj2nu3Z7/EOQTcsJWQydq52ARWznKvS3vsmAMFVhJFYmsjF2BeqbXseMLg6MRi4EagQPJfZWZ7Lwa6M7Pn3XgL7Wwzl37uIhJECbBEJl5eBM5xzLbCg5mznXKegx/EOLBD6I4SPNwo41znXPsjtfoTsB46DgDOAX/ex7S1sEF0TyBxEd2GwrRQW3G0AijjnHsLyjw/Hb1jv9mPBl4MIrMe1AdbDuRfnXDnn3KPOuaOdcxHOBj1eCfwV7LIOqOCcK5PlbqWwKiPbnXMNsUGC2TUSuNk5F+tsYOjALNuKYekxG4BU51xXLP/5YMe7N3gescBN2W2Ic668c+5S7AvFs0GazhosLeUF51zp4DWp55zLmgLzEXaF4FL2kx4ClMCC6A3BY13Bf19awF7X2AOMIfgYuMI51zL44vEUMClIyRGRAkABtoiEhfd+A9Yj/JD3fgFWfu5VLF/2XKykX3IIH28OFqCNwHpGt2N54Luycd9N3vsJQU7znttGA88CI4KUitnY4EGwnOlvsXJ/y7BBkyv2PEY2278FC0hPwHpAF2O9nsdjwdrV+7hbMlAb+AELmmdjz7dfcMz5WLC3JEh1qIYNCr0E2Ib10n5yCM0cjD3nf4Dp2KDBjPZvw0oEjgQ2B4/x5UGO9yj2ui3FAuPh2WjDP8657djgzquA27z3D2XZ3gcL9ucG7RiFDTTMaOckLI2mGlZlZS/e+7lYTvufWDDdDPg9yy4/AnOAtc65+H3c/wfgQaxHfw02iPOiPfcTkfzL7ePzQkSkwHM2yU0ClgqxNMzNERGRAkQ92CJSaDjnznXOxQQ5r88Ds7AKGyIiIiGjAFtECpNuWHrFaqA+cNG+0j5ERESOhFJERERERERCSD3YIiIiIiIhdNAA2zlX3Dk32Tn3j3NujnPu0WB9HefcJOfcIufcJxnliJxzUcHvi4LttbMc695g/QLnXJcs688M1i1yzg3Msn6fjyEiIiIiklcdNEUkmPChhPd+e1Cf9jfgFuB24HPv/Qjn3FvAP977N51z1wPNvffXOucuArp773s75xpj5aCOx8of/YDVbwUrYXUGsBKbNOFi7/1c59zIfT3GgdpbsWJFX7t27cN5LUREREREsm3atGnx3vtKe67f13S8uwkGAG0Pfi0aLB6btveSYP0wbNKGN7FBRI8E60cBrwVBejdstrFdwFLn3CIs2AZY5L1fAuCcGwF0c87NO8Bj7Fft2rWZOnXqwZ6WiIiIiMgRcc4t29f6bOVgO+cinXMzsEkZvscmOEjw3qcGu6wEqge3qxNMpBBs34JNhpC5fo/77G99hQM8xp7tG+Ccm+qcm7phw4bsPCURERERkRyRrQDbe5/mvW8JxGK9zg1zslGHynv/jve+jfe+TaVKe/XSi4iIiIjkmkOqIuK9TwB+AtoBZZ1zGSkmscCq4PYqoAZAsL0MsDHr+j3us7/1Gw/wGCIiIiIiedJBc7Cdc5WAFO99gnMuGhuM+CwWaPcERgB9gS+Cu3wZ/P5nsP1H7713zn0JfOScexEb5FgfmAw4oL5zrg4WQF8EXBLcZ3+PcUhSUlJYuXIlO3fuPJy7SwgVL16c2NhYihYtGu6miIiIiOSIgwbYQFVgmHMuEuvxHum9/9o5NxcY4Zx7AvgbeC/Y/z1geDCIcRMWMOO9nxNUBZkLpAI3eO/TAJxzNwLjgUhgiPd+TnCse/bzGIdk5cqVlCpVitq1a2PjLSUcvPds3LiRlStXUqdOnXA3R0RERCRHFLiZHNu0aeP3rCIyb948GjZsqOA6D/DeM3/+fBo1ahTupoiIiIgcEefcNO99mz3XF5qZHBVc5w06DyIiIlLQFZoAO9xWrlxJt27dqF+/PvXq1eOWW24hOTl5r/1Wr15Nz549D3q8s846i4SEhMNqyyOPPMLzzz9/WPcVERERkQNTgJ0LvPf06NGD888/n4ULF/Lvv/+yfft27r///t32S01NpVq1aowaNeqgxxw3bhxly5bNoRaLiIiIyOFSgJ0LfvzxR4oXL84VV1wBQGRkJC+99BJDhgzhjTfe4LzzzuO0006jU6dOxMXF0bRpUwASExPp1asXjRs3pnv37rRt2zZzlsratWsTHx9PXFwcjRo14uqrr6ZJkyZ07tyZpKQkAAYPHsxxxx1HixYtuOCCC0hMTAzPCyAiIiJSiGSnikiBcuutMGNGaI/ZsiW8/PL+t8+ZM4fWrVvvtq506dLUrFmT1NRUpk+fzsyZMylfvjxxcXGZ+7zxxhuUK1eOuXPnMnv2bFq2bLnP4y9cuJCPP/6YwYMH06tXLz777DMuu+wyevTowdVXXw3AAw88wHvvvcdNN910ZE9WRERERA5IPdh5wBlnnEH58uX3Wv/bb79x0UUXAdC0aVOaN2++z/vXqVMnM/hu3bp1ZpA+e/ZsTj75ZJo1a8aHH37InDlz9nl/ERERkfxo9WqYMCHcrdhboevBPlBPc05p3LjxXnnVW7duZfny5RQpUoQSJUoc0fGjoqIyb0dGRmamiPTr148xY8bQokULhg4dys8//3xEjyMiIiKSF3gPH34IN98MxYvD0qWQJRwKO/Vg54JOnTqRmJjIBx98AEBaWhp33HEH/fr1IyYmZr/3O/HEExk5ciQAc+fOZdasWYf0uNu2baNq1aqkpKTw4YcfHv4TEBEREckjdu6ECy+Eyy+HRo3g55/zVnANCrBzhXOO0aNH8+mnn1K/fn0aNGhA8eLFeeqppw54v+uvv54NGzbQuHFjHnjgAZo0aUKZMmWy/biPP/44bdu25cQTT6Rhw4ZH+jREREREwio5GXr2hM8+g2efhV9/hQYNwt2qvRWamRzz48yBaWlppKSkULx4cRYvXszpp5/OggULKFasWLibdkTy6/kQERGR8ElKsl7rzz6Dt96Ca64JNqSmQpHwZD3vbybHQpeDnZ8kJiZy6qmnkpKSgveeN954I98H1yIiIiKHatYsuPhimDMHXnopCK6Tk+G++2zl2LEQkXcSMxRg52GlSpViz954ERERkcLCe3j1Vbj7bihbFr79Frp0ARYtsoh76lS4/nrrxc5DnZAKsEVEREQkz1m/Hq64AsaNg7PPhiFD4KijsN7qSy6xHuvPP4fu3cPd1L3knb50ERERERHgm2+gWTOrcf3aa/DVV3BUxXR46ik491w4+mibOTAPBtegAFtERERE8ojERJt1+6yzrLd66lS44QZwK5bDGWfA/ffDRRfBxIlQq1a4m7tfCrBFREREJOx++QVatIBXXoGbboLJk6FpEw//93/QvDlMmgSDB9sMMweYRyQvUICdSyIjI2nZsiVNmzbl3HPPJSEh4YD79+vXL3P2x44dO2YOdjzrrLMOet/siIuLwznHAw88kLkuPj6eokWLcuONNx7x8UVERESyIzHRZmTs2BHS0+HHH2HQIIhO3Ai9e1ttviZN4J9/4KqrwLlwN/mgFGDnkujoaGbMmMHs2bMpX748r7/++mEdZ9y4cZQtWzYkbapTpw5jx47N/P3TTz+lSZMmITm2iIiIyMFMnQqtW1ulkJtvhpkz4dRTsXIhzZrBmDHw9NM2o0y9euFubrYpwA6Ddu3asWrVKgBmzJjBCSecQPPmzenevTubN28+4H1r165NfHw8cXFxNGrUiKuvvpomTZrQuXNnkpKSAJgyZQrNmzenZcuW3HXXXTRt2nSfx4qJiaFRo0aZveOffPIJvXr1yty+YcMGLrjgAo477jiOO+44fv/9dwAmT55Mu3btaNWqFe3bt2fBggUADB06lB49enDmmWdSv3597r777iN7oURERKRASk2Fxx+Hdu1g+3b4/ntLDSnBDiu717UrlC9veSIDB0JkZLibfEgKX5m+W2+1Uaeh1LIlvPxytnZNS0tjwoQJ9O/fH4A+ffrw6quv0qFDBx566CEeffRRXs7msRYuXMjHH3/M4MGD6dWrF5999hmXXXYZV1xxBYMHD6Zdu3YMHDjwgMe46KKLGDFiBJUrVyYyMpJq1aqxevVqAG655RZuu+02TjrpJJYvX06XLl2YN28eDRs2ZOLEiRQpUoQffviB++67j88++wywLwx///03UVFRHHPMMdx0003UqFEjW89HRERECr5//4U+fSyl+pJLrEpIuXLYissvtxrXt98OTz4JxYuHu7mHpfAF2GGSlJREy5YtWbVqFY0aNeKMM85gy5YtJCQk0KFDBwD69u3LhRdemO1j1qlTh5YtWwLQunVr4uLiSEhIYNu2bbRr1w6ASy65hK+//nq/xzjzzDN58MEHqVy5Mr17995t2w8//MDcuXMzf9+6dSvbt29ny5Yt9O3bl4ULF+KcIyUlJXOfTp06UaZMGQAaN27MsmXLFGCLiIgI3tsU53feCVFRMGKEpViTkgIPPW4l+KpVs9p8p54a7uYekcIXYGezdzjUMnKwExMT6dKlC6+//jp9+/Y9omNGRUVl3o6MjMxMETkUxYoVo3Xr1rzwwgvMnTuXL7/8MnNbeno6f/31F8X3+PZ44403cuqppzJ69Gji4uLo2LHjftuUmpp6yG0SERGRgmXNGujf3+pbd+5sk8ZUrw7Mnw+XXQbTplm39qBBEHTU5WfKwc5lMTExDBo0iBdeeIESJUpQrlw5Jk6cCMDw4cMze7MPV9myZSlVqhSTJk0CYMSIEQe9zx133MGzzz5L+fLld1vfuXNnXn311czfZwSpNVu2bKF69eqA5V2LiIiI7M+oUdC0Kfz8s6WDfPstVK+absF0q1YQF2c7DRtWIIJrUIAdFq1ataJ58+Z8/PHHDBs2jLvuuovmzZszY8YMHnrooSM+/nvvvcfVV19Ny5Yt2bFjR2bKxv40adJkn73pgwYNYurUqTRv3pzGjRvz1ltvAXD33Xdz77330qpVK/VQi4iIyD5t2WKd0hdeaAVA/v47y6QxXbrALbfAaafBrFlwwQXhbm5IOe99uNsQUm3atPEZVTEyzJs3j0aNGoWpRblv+/btlCxZEoBnnnmGNWvW8Morr4S5Vf8pbOdDRESksJk0CXr1glWr4IEHbALGokW8TRRz551W8Pr55+Gaa/JFXev9cc5N89632XN94cvBLgTGjh3L008/TWpqKrVq1VIah4iIiOQK7+GNN+C222y84u+/Q9u2wLJlNknMDz9Yr/W770KdOuFubo5RgF0A9e7de6+KICIiIiI5adkyuPZay7E++2z44AMoX87D2+9YrzVYGZEBA/J1r3V2KAdbRERERA5baqoVaWvSBCZOtLGLX34J5bfGwRlnWNR9wgkwe3a+TwnJLvVgi4jkM97Djh2QmAhFi0KxYhATUyg+s0Qkj5kyxWLmv/+2yRfffBNqVUuBlwbBww9DRAS8846lhxSiNykF2CIiedi2bTB+vF1ynT8fliyB9eshLQ2KkEIL/qEtk2gcuYBGxRZTOWIDpd12otwufFRxKB6Ni4kmomQ0RSqWI6rGURSveRSu8lFQuTLExkKNGlCpkn0Qiohkw5YtNnDxjTegShUYORJ69gT35x9w7rVWGeTcc60uX82a4W5urlOALSKSx3hvl1lffx3GjIHkZChbFlq0gJ4d4+mw5UtaLB1NrUUTKJpiE0ztjCzFuuL1WBdZlZUptdm2qxhu206Kk0Q0SUSzjXIs5yjWE83mvR4zvUgxiI0lon49aNzYitaecILdVuAtIgHvbQbGO+6AtWut7N4TT0CZ1I0wYKANXqxRw968unULd3PDRgF2LomMjKRZs2akpqZSp04dhg8fTtmyZfe7f79+/TjnnHPo2bMnHTt25Pnnn6dNmzacddZZfPTRRwe8b3akp6dz66238uOPP+Kco3jx4owcOZI6derw1FNPcd999+3zfo888gglS5bkzozBCiISMt5bb/UDD9ikZmXLWuriBT087SMnUeTt162bKDkZateGa/rDySdDu3YUj42llnPUynK89HRISID4eNiwAeashe9WwboVyWxbGs/OpWtIjVtJzKYV1ExdTs24FbSIX0S9X96lWPIOO0jZstCpE5x1lo1aqlw5118XEckb5s6FG2+En36C1q3hiy/guDbeJoi56y7YvNl+PvQQBOWCCysF2LkkY6p0gL59+/L6669z//33H/Jxxo0bF5L2fPLJJ6xevZqZM2cSERHBypUrKVGiBMABA2wRyRkTJ9rl1okTLXYePBguuWAXMaM/hNtfh+nToVQpG31/5ZXQsuVB8xkjIqB8eVsaNMi6pRhQLVhas307/PUX/PILPDkG5sxO55jIxdzW9g8uqDyRCn99C599Zgc8/XS4/HIrcFusWA69GiKSl2zfDo8/Di++aHHzG2/YW1Hk/DnQ4Tp74zrxREvAbtYs3M3NE3TdLwzatWvHqlWrAJt+/IQTTqB58+Z0796dzZv3vnSbVe3atYmPjycuLo5GjRpx9dVX06RJEzp37kxSkl0qnjJlCs2bN6dly5bcddddNG3adK/jrFmzhqpVqxIRXPqNjY2lXLlyDBw4kKSkJFq2bMmll14KwJNPPkmDBg046aSTWLBgQShfCpFCb9o0Gxh0yimwaJF9cC2Ym8ZVRYcR07IB9O8Pu3bZhlWr4NVXbWrhEA4WKlnS4ubHH7e0yZmzIuhyY31u/6cvFUe/S+dGK/jjjRn4ewbCv/9agF23rk0SsXVryNohInmL9zaDeaNG8L//2ayM//4L1/XZQeT9A+2L/pw58N578OuvCq6zKHQ92LfeemtmT3KotGzZkpdffjlb+6alpTFhwgT69+8PQJ8+fXj11Vfp0KEDDz30EI8++mi2j7Vw4UI+/vhjBg8eTK9evfjss8+47LLLuOKKKxg8eDDt2rVj4MCB+7xvr169OOmkk5g4cSKdOnXisssuo1WrVjzzzDO89tprma/RtGnTGDFiBDNmzCA1NZVjjz2W1q1bZ6t9IrJv3sPkyfDcc9YxXL683b7+Ok/Mj1/D8fdZOavWra0r+4wzcnX0fdOmVnLr4Ydt8P8rrzhO/KEFLVu24NGXn+DcYuNxz/3PLgU/8QRcdx3ceqvSR0QKkH//hZtugu++s/Efn3wC7dtj9fduvtmKXl95JTz7LFSsGO7m5jnqwc4lGb3CVapUYd26dZxxxhls2bKFhIQEOnToAFjqyK+//prtY9apU4eWLVsC0Lp1a+Li4khISGDbtm20a9cOgEsuuWSf942NjWXBggU8/fTTRERE0KlTJyZMmLDXfhMnTqR79+7ExMRQunRpzjvvvEN85iKSIS3NBtQ3amTjB7/7Dh55BJYuhTu7LSSmawc47zzrsR450qLwzp3DVtqqXDm45x6Ii4P337eKJt3Od5zwyJlMevrH/9r3v/9B/fr2LSE5OSxtFZHQSEy0cSDNmlnq2KBBMHUqtI9dDuefbwMXS5a0tJD33lNwvR+Frgc7u73DoZaRg52YmEiXLl14/fXX6du37xEdMyoqKvN2ZGRkZorIody/a9eudO3alcqVKzNmzBg6dep0RG0SkX1btgz69rU85/btbaD9hRdC6VLeuolvvx2iomyWsyuvtALXeUSxYtCvH1x2mc3M9uCD9gWhf//jePr1kVR6fIHN0nb33dbj/uKLNiCyENW8FcnvvLfO6Vtusferyy+3785VKqTASy9bbwBYj/Vtt+Wp96i8SD3YuSwmJoZBgwbxwgsvUKJECcqVK8fEiRMBGD58eGZv9uEqW7YspUqVYtKkSQCMGDFin/tNnz6d1atXA1ZRZObMmdSqZfUHihYtSkpKCgCnnHIKY8aMISkpiW3btvHVV18dUftECqPRo6F5cxunOHQo/PabpVaXTlpnPdbXXmtR96xZNmNDHv3gKlLEYv/58y07ZNgwOOYYePPHY0gb8xV8840NhDz3XKs6snhxuJssItmweDGcc451UJcsaR0BH3wAVRb9Bscea1+ezzjDyojcfXeefY/KSxRgh0GrVq1o3rw5H3/8McOGDeOuu+6iefPmzJgxg4ceeuiIj//ee+9x9dVX07JlS3bs2EGZMmX22mf9+vWce+65NG3alObNm1OkSBFuvPFGAAYMGEDz5s259NJLOfbYY+nduzctWrSga9euHHfccUfcPpHCIjUVBg6EHj0sEP3nH+vFdg4LRps2hR9+gFdesfp81auHu8nZUqqU9Wz984+Ncbr+eqsWuPSYM+1Lwosvwh9/2LeKV16x3BgRyXOSkqxjukkTG6P4wgs2I+MpjeOtF+Dkk20g8xdfWF3rWrUOdkjJ4L0vUEvr1q39nubOnbvXuoJs27Ztmbeffvppf/PNN4exNXsrbOdDCqfERO/PO8978P6aa7zfuTPYkJbm/aOPeu+c9y1aeD9nTjibecTS070fPtz70qVtGTEi2LBihfdnn20vQMeO3q9ZE9Z2isjuxo71vm5d+xe9+GLvV63y3qekeP/GG95XqOB9kSLe332399u3h7upeRow1e8jHlUPdgE0duxYWrZsSdOmTZk4cSIPPPBAuJskUqhs2QJnnglffWWDGt96y9KrSUqCCy6w8hyXXWa9vI0bh7u5R8Q5eyozZthTuegi6/jaUS7WXoD334dJk6y04CEM4haRnBEXZ6kgZ59t4ysmTICPPoJqs7/775JUkybWlf3ssxDMkSGHRgF2AdS7d29mzJjB7NmzGTt2LJUqVQp3k0QKjc2bbeLDP/6ADz+0aYQBm6nh7LPtUuvLL1sCc0xMOJsaUnXqWPx8//0WU7duDXPmOhsdOWkSlC4Np51muSXeh7u5IoXOrl3w5JP2Rfj77y12/ucfOK3afEvA7tLFOgE++wx+/tlS2OSwHTTAds7VcM795Jyb65yb45y7JVj/iHNulXNuRrCcleU+9zrnFjnnFjjnumRZf2awbpFzbmCW9XWcc5OC9Z8454oF66OC3xcF22uH9NmLiITQ5s02DmjWLEtXvPjiPTb8+quNHLrllgJZYaNoUSuLPWGC9eKfcIJVJaBZM5gyBbp3t7p/55+vCWpEcon3NuSjWTMrv3f22TZQ+e4eiyg2oJ8F0hMn2pffuXNt0EgBfH/KbdnpwU4F7vDeNwZOAG5wzmVc03zJe98yWMYBBNsuApoAZwJvOOcinXORwOtAV6AxcHGW4zwbHOtoYDPQP1jfH9gcrH8p2O+wePWY5Ak6D1JQJSRYSehZs+Dzz+1DDID1663ndto0+PRTy6co4E491eLphg0tln7uOawHe+RI670fOxZOOglWrAhzS0UKrozAun17K+oD8O238Okzi6nx0BX2D/rJJzZpzMKFVhooS/lfOTIHDbC992u899OD29uAecCBhrp3A0Z473d575cCi4Djg2WR936J9z4ZGAF0c8454DRgVHD/YcD5WY41LLg9CugU7H9IihcvzsaNGxXchZn3no0bN1K8ePFwN0UkpDKC63/+saurmcH1qlXQoQMsWGD5yN27h7OZuSo21jrse/e2ql733AMeZ73348ZZIugJJ1iep4iETEY96+OPt8B6zRp4802Y9cUSunxypZU0GjHCpmlcssSq/hx1VLibXeAc0kQzQYpGK2AScCJwo3OuDzAV6+XejAXff2W520r+C8hX7LG+LVABSPDep+5j/+oZ9/HepzrntgT7xx9Ku2NjY1m5ciUbNmw4lLtJDihevDixsbHhboZIyGzZYqmLM2ZYcH3OOcGG9evh9NNh5UrrNjrllHA2Myyioy0PvUIFu/q8aZMN+Izs3Bl+/92+iZx8svVsn3XWwQ8oIvuVnm419x9/3L7s161rEy1eftJSij77BNw4zIrZ33CD1Q+tWjXcTS7Qsh1gO+dKAp8Bt3rvtzrn3gQeB3zw8wXgyhxp5cHbNgAYAFCzZs29thctWpQ6derkdrNEpIBLSrI5Vf7+G0aNstuAdWl36WLToY0fb0FkIRURAa++CuXL2wd/QgL83/9BVMY8zOeea8vrr9uEOyJySNLSLPvsiSdgzhxo0MDGUF/SPo4izzwB1wyDyEirDjJwIFSrFu4mFwrZqiLinCuKBdcfeu8/B/Der/Pep3nv04HBWAoIwCqgRpa7xwbr9rd+I1DWOVdkj/W7HSvYXibYfzfe+3e89228921UMUNEckNaGlx6qY0N+uADm5ARsGohZ51ln3Sff16og+sMzsFjj8FLL/33RWT7duyD/pdf7PW67jqLEJTKJ5ItSUkweLBV1Lv4YvvX+egjmDsujj6/DaBIo/owfLh9cV28GAYNUnCdi7JTRcQB7wHzvPcvZlmf9dpCd2B2cPtL4KKgAkgdoD4wGZgC1A8qhhTDBkJ+GRTp/gnoGdy/L/BFlmP1DW73BH70SqQWkTDz3jqDRo+2iQovuijYsHOn5VlPmgQff2zFsCXTrbdaCb8JE6yoyqZN2LzMo0fD5ZfDgw/CnXcqyBY5gHXrrJR+zZowYIBV+/z0U5j19TIu/vkaIhvWty7sa66xwPrVV/PNLLEFSXZSRE4ELgdmOedmBOvuw6qAtMRSROKAawC893OccyOBuVgFkhu892kAzrkbgfFAJDDEez8nON49wAjn3BPA31hAT/BzuHNuEbAJC8pFRMLq0UfhnXfg3nttAD5g86JfdJFNfT50qE0oI3vp1w/KlrXBjx06wHffQdWqRew1K1vWBlxt3mwvcJFDGiYkUqCtWGFpVsOGQUqKXQm6/XY4pfZy3NNPwSVD7HLRgAH25qTxTmHlClqHcJs2bfzUqVPD3QwRKaDeesuyGa64wgYQOYeNLrrySvvke/VVuPHGcDczz/vxR+jWza5Y//xzMN7Ke/v28uijVov3o49UNkwKvfXr4amnrBII2FvNbbdBg0qbLa3q1Vdtw1VXWWBdo8b+DyYh55yb5r1vs+d6zeQoIpJNn39uqSHnnGMdrM5hQeGdd1pw/eijCq6z6bTTrEbvqlVWN3vtWuwFfeQRq5X9+ef2Qm/fHuaWioTH1q02MUzduhZDX365lat+85VkGnzzChx9tA1suOwyWLQI3nhDwXUeogBbRCQbfvkFLrnESjd/8kmW7IWnnrIPuZtvthxiybaTTrIge+XKLEE2WK3s99+3bu7MZG2RwsF7K295zDE2tfk559gEi+8O9tScNtpGNd56Kxx7rJUwGjLEErIlT1GALSJyEDNnWpWQunVtvpiYmGDDW29ZF9Nll1mQremFD9nJJ9u8MytWWK/2unXBhn79rOTI9OmWrL1mTTibKZIr5syxL5uXXWad0VOm2Jwwx2ybav8HPXpA0aI2G+p330GLFuFusuyHAmwRkQOIi7NiIKVKWUnrChWCDZ988l++yJAhVvBZDsspp1i8sGyZBdmrMgq1du9u0ffSpdbdvWRJWNspklO2bbNMs5YtYdYsePttKxPf5qjlFm0fdxzMn2+J2DNnWmlLfaHP0/SJICKyHxs22HwxSUkWXGemN44bZx96J51ksxAWLRrWdhYEHTr815Pdrh3Mmxds6NTJ6vpt3myv9/TpYW2nSCh5b9/VGzaEF16wCzcLFsCAi7YS8cB9lify2Wc2eHHRIqtpreo6+YICbBGRfdixwzqnly+Hr7+2tEfAkrEvuACaN7d8kejosLazIOnQwV7e5GQ48UT49ddgQ9u2NqNP0aKWU/LFFwc8jkh+MH8+dO5s1T2rVIE//4TBb6ZScdRbNoDx6aehZ0+LuJ96CkqXDneT5RAowBYR2UNKin2uTZ1qvUsnnhhsmDrVis/WqWNd2mXKhLWdBVGrVvDHH3DUUdZ5/eabwbwzTZrYBD5NmljqyIsvakIayZc2bIA77rDv6FOnwuuvw+RJnhM2jrWc6uuug0aNLAF7+HANYMynFGCLiGSRlmY1rr/91krxZU6BPmeO5YtUqADffw8VK4a1nQVZ3boWS3fpYmnu11wDu3Zh3Xw//2wDve64wwKRlJRwN1ckWzZuhPvvt+/nL79sWWYL5nuurzeeyJPb2yWzlBSb2fTnn6HNXqWVJR9RgC0iEkhLs0kcPvzQrs727x9sWLLEysVFRdlMjZp2OMeVKWOZIPffD4MHW2WFNWuwEi4jR8LAgTYSrHNnWL063M0V2a/4eEuhrl3b3lfOPhvmzPYMufh7jupxko2iXr3a/p5nz4bzz9cAxgJAAbaICP8F1x98YNMRDxwYbFi1ynIVdu2ynut69cLazsIkMtImqvv0U/jnH2jd2sY7EhFhkcqwYTB5spVeGD8+3M0V2c3q1XD33RZYP/usdVDPnp7MJ90+omG/E+zL4fLllgf17782xXmxYuFutoSIAmwRKfT2DK4feCDYsGGD9Vxv3Gg5I5kjHSU39expA8DKlLHTcffdNhCSPn0sT7VyZesFHDhQKSMSVunp9l2vRw9LnX7hBejWDeb/so6PGz1G46614NJLISHBkq8zKoNERYW76RJiCrBFpFDLGlw/9liW4Dox0bqcli61aiHHHRfWdhZ2zZvDtGnWyffcc9C+vRVXoHFj68UeMMC6CTt0sILaIrlo3Tp45hmoX9++602caMMEln02lQ8j+9CgUw14+GEbxfvNN1aH8vrrFVgXYAqwRaTQ8h5uvPG/4DpzpvP0dOjb13pHP/7YgjYJu5gYmzxz9Gj73nPssXZ1PT0q2vJXR4ywHNaWLWHMmHA3Vwo47+Gnn6B3b6uRf++99nPE8BRWvziCZye2J7b7cfYHe+219o1w3DiLwDUxVYGnMywihdbzz1vAds89WYJrsJF1o0ZZV+n554erebIf559vk9m1b2+dgCefbEVe6N0b/v7b8uS7d7dvT0lJ4W6uFDAbN1qVyIYNbebR77+HG26ABRPX8/PpT9D7ntoU7XOxpZi98oqN4xg0CBo0CHfTJRcpwBaRQmnUKMvl7d3b5nDI9Mordq13wAC4/fawtU8OrHp1+O47GDrUOgZbtrTvRUnV6sHvv8Ntt1mOa5s2Fo2LHKEZMyztv3p1S/+oWNHG2a4eN4OXEq6wNJAHH7R8pnHj7A/z5ps1QUwh5XwBK9Tfpk0bP3Xq1HA3Q0TysD//tJ6nY4+1qhTFiwcbPvzQitN2726l4DQlcb4QH28Bzwcf2AR4w4fDCSdgEXjfvrBpk+Vn33yzLs3LIfHeeqife84qdJYsaUH2NVel0TzuS/tC/ssvUKKE/a3ddJN1bUuh4Zyb5r3fq2i53mlEpFBZvNgmj4mNtTrLmcH1d99Bv35WcPmjjxRc5yMZPYk//ACpqXDKKfDaa+DP6Gy912eeaT3aXbvaaDSRg0hOti9sLVrYhEdz5lhlyBVzt/F6g1dofkF9KxUSF2e5ZitX2hUTBdcSUIAtIoXGpk02yUN6ul3BzZyM8Z9/rBZc48Y2OC4z6pb8pFMnmD7dAqKbbrJqaNujK9k5ffNN+PVXi5gmTAh3UyWP2rrVeqvr1LEO6fR0eP99WPrzMgbG30nZprFw661QrZrlmS1aZJdPypYNd9Mlj1GALSKFwq5dlvmxdKn1XNevH2xYudKi7tKlYexY5Uvmc+XK2fl98kn45BNo2xbmL3BWxWHKFChf3oppP/SQdXeLYGlGDz0EtWrZ2IyGDa2a3qx3J9Hvm95ENa5n85ufdRZMmgS//QYXXKArXbJfCrBFpMDz3mpd//qrDYo76aRgw9atFlxv3Wpd2rGx4WymhEhEBNx3n034sX695WP/9BPQtKkF2f362YxCnTpZhQcptFatsg7oWrXsT+K002DKn6lMuG4UZz7WHtfuBPtDuv12WLLEynYef3y4my35gAJsESnwHn7Y0qqffBIuvjhYmZICF15oyZWjRtnIfylQTj/dJqepXt3SRj76CBuMNmSIJdhOm2YpI+PGhbupkssmT4ZLLrFpzF95xTqj503aymcnvUSbi+vbe8O6dVZeb8UK+N//bGpGkWxSgC0iBdrQodYz1b+/TQQBWJf2tdfawMZ33oHOncPZRMlBNWva1fz27S0n+9ln7fRz+eX/Rd9nnw133gk7d4a7uZKDEhNtLqL27S11aOxYy9VfOmEJH1S4jYZn1LCe6ho14PPP4d9/bYdSpcLddMmHFGCLSIE1YQJcfbX1ZL75JjgXbHjySevFfPBByx2RAq1cObvKf9FFMHCgzT+TlgYccwz89Rdcdx288IL1Zv/yS7ibKyGUng5ff2091BUr2hWs9eth0CueNR/9xItLzqfGqUdb2Zmzz7au7V9/tQEbkZHhbr7kY6qDLSIF0ty51lNVo4b1YJYpE2z4v/+z3svLL7fabplRtxR06ekWYD/3HJx4on3Hypxc77vv7KrG0qVWC/3JJ5USkI9t22ZXrwYNskIfVapYkN3j3BQ6rvuEiBeft+pBFSvCNdfYl6zq1cPdbMmHVAdbRAqNtWttsH90tF0Gzgyuf/rJeqxPPRXefVfBdSETEWGptMOHW+p9ixZWOWL1aixNaNYsyyMaNcoi75tvhnnzwt1sOQTr1lm2T2ysnb5KlSwtZPnsrbxW5wVOu6ouEX0vtzEY774Ly5fDE08ouJaQUw+2iBQoO3ZAx47Wg/3rr9C6dbBhzhzrtqxe3abSVt3aQm3NGgvARo2ySmvnnmsZAqefDrXccou8P/rIArH27W3jGWdAs2aqk54HbdpkVyYGDbKSnBdeaOWq2zZLtPSf55+3akEdOsBdd9mkQ5rVU0Jgfz3YCrBFpMBIS7PLwF99ZXOLnHtusGHNGqvVlpxsObe1aoWzmZKHLFoEb78NH35ofyZgvZ5Nm0Ljius5feX7HL/4Y6qt/weAdBfBtkp12VyyJvHp5UgsU5W217Um6uTjrXiygrZctW2bVQF57jm7fdFF8OijUP9ob13X99xjVUDOP99qNx53XLibLAWMAmwRKfBuu83mghg0yAb/A7B5s/VaLVliXdrHHhvOJkoe5b1d9fjxR0vNnT3bUo22bbOlXMo6OvIzjZlLI+ZRjdVUitxEtbQVlGK7HaRUKWjTxgqtd+wI7dpZnpLkiPHj4aqrbK6obt2sWlCzZthAxVtvhT//hFat4KWX7D1AJAcowBaRAu3FF23CiFtusSAbsHyRzp1h6lRLxj799HA2UfKx5GQLtFNTbSlTBkqWhCHvpvPc1Qu4puVk+jebTKm5k+Dvv21EZbFiduXk1FPhvPMs2FPe/xHbutX+1999Fxo1sp/t22N1+O67z75hV64MTz0FffqoGojkKAXYIlJgvfee9WT17GlXhSMjsZrG3brBDz/AyJGWOyKSA15//b8rJmecAb3O3Ern6InELvoZ98vPMH26Bdw1a1qFkiuvhHr1wtrm/Oq77+x/fdUqS6V+5JEgJf633+CKKyzn56abrAqM6ldLLlCALSIF0qefWt5l587wxRfWacjOnZZz+d13Fn1fcUW4mykFXFwcvP++VX5ctszWRURY8Neg3Abubvw1Zyd+Sqk/x+PS0y2NpEcPq7dcu3Y4m54vJCdbOvXLL1uq+9ChNlkMSUnwwAOWBlK7ttVe7NgxrG2VwkUBtogUON9+a1fe27a1fMyYGCy47t7dNr73niaSkVzlPSxeDD//bIF2UpL9PnasFSTp1HAVj9QeStvlIyk6d6bdqVUr+5u96CKoXz+s7c+L4uKgVy+YMsUqvzz7bNBr/eef0K+fzbh4ww3wzDOWtyOSixRgi0iB8ttv1mvdsKGVty5TBguue/SAb76xxMz+/cPdTBEANm6ETz6BDz6ASZMgKgru6rGYW2qNpuKvo+GPP2zH44+3HIg+fWynQm7MGLsA5b11TvfogX1reeghG3hRo4ZtOO20cDdVCilNNCMiBcZPP9lEMjVrWkd1mTJY8dsLLrDgevBgBdeSp1SoANdfb1Ui5861+Pl/n9Wj8v/u5JJavzNn/EqrNbdzJwwYAHXrWv3m7dvD3fSwSEqyQiDdu8PRR1sae48e2AvYqpXVtR4wwCYHUnAteZACbBHJV0aNgjPPtOB6wgQ46igsuO7RA8aNg3fesR5AkTyqUSP7M1261KphfPUVNO1SnbN+vJNfB83Af/e9XZq58077Q3/4YSs3WUhMnmzVNF95xcYr/vYb1C0db2kgJ55o0ff338Obb2ogo+RZCrBFJN944w3LxTzuOJg4MZjdOKPnetw4mzHk6qvD3UyRbKlWzaZuX77cil5MnQodOjo6PnE6U56ZYL21p5wCjz1mA/gefRS2bAl3s3PMrl1w//1Wcm/7dhtXMej5ZKLefNly099+2y4DzJqlkpuS5ynAFpE8z3tLubzhBjjnHCsOUq4cVve2Rw8bQfbWW3bJWCSfKVfOyjcvWwavvgrz5lkq9uWvtWXd22Ns5pvTTrOadHXqwNNPF6jUEe/tatTxx1vp6ssvh1kzPZ13fWUzx9x2m41knjnTXqDSpcPdZJGDUoAtInnajh2Wr/r441YQ5PPPg2ohCQnQpYvlXL/9NlxzTbibKnJEoqPhxhutlPN991n59oYNYfCk5qR/Ntq6uNu3t4116lgecmJiuJt92DZtstKGrVtbh3R8PHw1Jo33u46k7GnHWokggK+/tv/zxo3D22CRQ6AAW0TyrLlzrVfrww/tKvm770KRItgc1h06WDmGTz5Rz7UUKKVLW8rIzJnQooX9ebdoAaOWtib9y68tdeTYY22mlbp1LVl5585wNztb0tKsXn2XLjZ+4sor7TvCkDd2EvfQEM65qxH07m151kOGWDrI2WdrBkzJdxRgi0ietGqVpZ/Gx9t4pgcfDD5jlyyxgU4ZxYUvvDDcTRXJEcccYxVzPv7Ypme/8EIroDFmTVv8t+NtIEKjRlZu4+ijLXVkzZpwN3ufVq+2MtUNGtgcUPPmwV13eua9+zvzOlzLFfdVpei1/W3Q4qefwpw5Vp+vWLFwN13ksKgOtojkOWlpNuX05MkwbZoFGoB16XXpYtO6jRsXTOUmUvClpcGIETbOceFCC1R79LBg9bjtPxHx5OMWjUdGWo/vVVdB167BJZ/w2LULvvzS0kDGj7fZ4k8+Ge67YAFnbBpB5IfD7YtyTIw9mX79LNdcvdWSj2iiGRHJN55+2tJMhwzJMsv577/bCMcSJWyUo/IxpRBKTbUe7WHD4Jdf7PeqVS1duUezhZy8cAjRnwy1NKrKla1g/NlnW5JzmTI53r7ERPv3/PxzKz+YkACxsXBrt6X0jf6Eit+PsEGbzlkw3aePBdeagVHyKQXYIpLnZczWds010LOnBRLOYYOcevWyT+rvv4datcLdVJGw27zZLuSMGWMTLm3fbv8v7dqkcGfjcZwe/zElfx+PS0iwnuyTTrLpT08+2WpdhmimyIQEy9YaPdrGIiYmQvny0Pe0FVxT/lMa/P0Jbspk2/mEE2xK+J49gzqbIvnbYQfYzrkawAdAZcAD73jvX3HOlQc+AWoDcUAv7/1m55wDXgHOAhKBft776cGx+gIPBId+wns/LFjfGhgKRAPjgFu8935/j3Gg9irAFsmftm61wHrECOvY+vxzKFPa22x2d99tg7rGjQtmlhGRrJKTLaXqxx8tLWPaNFtftVIql9f/i3Mjx9Fi5VhKLZ1pG6KiLMXq5JNtJPGxx1rAm830jLVrbbDi6NH2mCkpUKvKLm47/nfOL/4tNed+i5s9y3Y+9lgbuNirl9XzFilAjiTArgpU9d5Pd86VAqYB5wP9gE3e+2eccwOBct77e5xzZwE3YQF2W+AV733bIFieCrTBAvVpQOsgKJ8M3AxMwgLsQd77b5xz/9vXYxyovQqwRfKfqVOtUysuzqqF3HMPRKbuguuuswTOnj1h6FBLDxGRg1qyxHqTp0yxwHvePFtfgXgurPIb55T+lZbbJlJ17XQifLptrFjRguHmza06SZ06FhDXro2PKs68eXbMMZ+nM+ePLdRkGSdWWUL3On/TJn0KZWb/htuxA4oWtcC9Sxeb67x+/bC9DiI5LWQpIs65L4DXgqWj935NEIT/7L0/xjn3dnD742D/BUDHjMV7f02w/m3g52D5yXvfMFh/ccZ+Gffd8zEO1D4F2CL5h/fw8ssWUFepYikhJ54IbNhgeZm//WYzzDz8MESo6JHI4dqyxXq1J0+26pYzZtgMktHp22nOTFrxN+2KTadNkb+pt3MORdOTd7t/EsXZRRQOTym2EUGW2CEyEpo0sX/erl3h1FOVUy2Fxv4C7EMaXuycqw20wnqaK3vvM+oBrcVSSACqAyuy3G1lsO5A61fuYz0HeIw92zUAGABQs2bNQ3lKIhIm8fFWNGDsWKuE8N57lrfJ5Ml2OXntWssX6d07zC0Vyf/KlLHUq9NO+2/drl2wdGlJFi5sz8KF7fl9IQxdCCuWpVN041oqbltK0xJLaRQdR+2K26hdZRexsRBRozSULWtjIWrXtlKBMTHhemoieVK2A2znXEngM+BW7/1WlyVPK8iXztHRkgd6DO/9O8A7YD3YOdkOETlyP/5o0yHHx9vMxzfcAC4tFR57ynJEqlWDX3+1gVgikiOiomymyIYN99wSAVQLlhNzvV0iBUG2rrk654piwfWH3vvPg9XrgrSNjDzt9cH6VUCNLHePDdYdaH3sPtYf6DFEJB9KTrbxiqefbvNJ/PWXTQ3tliy2nM2HH7Ye65kzFVyLiEi+ddAAO6gK8h4wz3v/YpZNXwJ9g9t9gS+yrO/jzAnAliDNYzzQ2TlXzjlXDugMjA+2bXXOnRA8Vp89jrWvxxCRfGb+fKvQ9dxzNvXz9OnQqmVQl69lSxuF9dFHNi962bLhbq6IiMhhy06KyInA5cAs59yMYN19wDPASOdcf2AZ0CvYNg6rILIIK9N3BYD3fpNz7nFgSrDfY977TcHt6/mvTN83wcIBHkNE8on0dHjrLbjzTkvTHDMGunXD8kMuG2B1vjp2tJkzNIZCREQKAE00IyI5ZsECuPpqmDjRpj4fOtTSqxk9Gq6/HjZuhKeegttvV5UQERHJd/ZXRUSfaCIScikp8OST0KIFzJplFULGj4dqbg1ccIGV4Ktc2SqG3HmngmsRESlQDqlMn4jIwUyZAv37W2B94YUwaBBUqRzkWt95JyQlwdNPwx132IQUIiIiBYy6jUQkJHbssJj5hBMs82PMGBg5EqpsWwidOsFVV9kMcTNnwsCBCq5FRKTAUoAtIkds/Hho1gxefNFyrufOhW5dky2/ulkzKxny9tvw00/QoEG4mysiIpKjlCIiIodt/Xq47TarrnfMMfDLL3DKKViB66uvhtmzoWdPyxOpWjXczRUREckV6sEWkUOWng7vv28zJH/6qc0P888/cErLrXDTTdC+PSQkwBdf2A4KrkVEpBBRD7aIHJLJk+GWW6yT+qSTLPOjcWPgq6/guutg9WoLsp94wqZrFBERKWTUgy0i2bJlC1xzDbRtC0uXWlGQX36BxpU3wmWXwXnnQfny8Oef8MorCq5FRKTQUoAtIgf13XfQtCm8+67NCfPvv3DFFRDx5Rho0gQ++cTyRKZOtQhcRESkEFOALSL7lZICd98NXbpYh/Qff8ALL0DppHVwySXQvbvlV0+ZAo88AsWKhbvJIiIiYacAW0T26d9/oUMHeO45uPZamDYN2rZJg9dft5Iho0bBY49ZUnbLluFuroiISJ6hAFtEdpOaCv/7n01zPm+eZX+8+SZEz5wExx8PN94Ixx1nUzU++KAmjBEREdmDAmwRyfT999YZfc890LWrTRjTq9NGGDAA2rWDtWthxAhLyj7mmHA3V0REJE9SgC0izJgBZ58NnTtDUhKMHg2ff5pG1XHvWSA9ZIjNKDN/PvTuDc6Fu8kiIiJ5lgJskUJswQKLl1u1sgGMzz4Lc+d4zo/8yrqyr7rKZpP5+28b3ajSeyIiIgelAFukEFq+HPr3twlixo6FBx6w2tZ3t/+NqNNPtprWO3daOsivv0KzZuFusoiISL6hmRxFCpFNm+Dpp+HVV+33W26BgQPhqHWz4PL74OuvoUoVG9XYv78GMIqIiBwGBdgihUBSkgXVTz9tMzL27QuPPgo1/TK46yEYPhxKl4annoKbb4YSJcLdZBERkXxLAbZIAZaWZrHzQw/BihVw1lnwzDPQrMoGePJJ66l2Du64w7qyK1QId5NFRETyPeVgixRA3sO4cTZ48YorLOvjxx9h7IhtNPv8Uahb17q0L78cFi602WQUXIuIiISEAmyRAmbKFDjtNCu7l5hoE8VMmpjMqbNfhXr1bErzM86A2bPh3XehRo1wN1lERKRAUYAtUkAsWmQl944/HubMsQ7qubPT6ZX6Ea5RQ8utbtIE/voLPv/cyu+JiIhIyCnAFsnn1q+Hm26yePnrr2328kWL4MZjvqdY+zZw6aU2gPGbbyxPpG3bcDdZRESkQNMgR5F8assWePFFW5KSbE6Yhx+GqmumQ8+BNu95rVo2yvGSSyBC36dFRERygwJskXwmMRFef92qgWzaBBdcYAVBjim2FO58AD76CMqXt8j7+ushKircTRYRESlUFGCL5BPbt8P771st6zVr4Mwz4YknoHXphbZy+HAoUgTuvRfuvhvKlg13k0VERAolBdgiedzKlfDaa/D225CQACedZDOYnxI1CZ553gYsFitmvdV33w3Vq4e7ySIiIoWaAmyRPGrqVHjpJRg5EtLToUcPuP2WNNrFfwX3PQ+//2691HffDbfeCpUrh7vJIiIiggJskTwlLQ2+/NIC64kToVQpqxByy4Akav08DK580SaGqV0bXnkFrrwSSpYMd7NFREQkCwXYInnAtm2WX/3KK7BkicXPL74IV523nlLD34CTX4f4eDjuOJs5pkcPy7cWERGRPEef0CJhtHIlDBoE77xjZffat4dnn4XzGy2gyKsvQdNhsHMnnHce3HmnJWA7F+5mi4iIyAEowBYJg9mz4bnnrKKe99CzJ9x2G7RlktXf++ILG7jYt69taNgw3E0WERGRbFKALZKLJk60HuqxYyEmBm64AW69xVN7yY9w31M202K5cvDAA3DjjXDUUeFusoiIiBwiBdgiuWDqVLjvPptcsWJFeOwxuP46T4W/xsJFj8PkyVC1Kjz/PAwYYKMbRUREJF/S3MkiOWj+fEv/OO44mD7d4udlcZ4Hj/uWCme1hXPPhQ0brMj1kiVwxx0KrkVERPI5BdgiOWD5cqug16QJjB8PDz0UxM8tfiDmjBOha1dYvx7efRcWLLBe6+LFw91sERERCQGliIiE0IYN8NRT8MYb9vvNN1tqSKW4KdDjXpgwAWJj4a234IorbCCjiIiIFCgKsEVCYOtWeOEFq12dmAj9+sHDD0PNnf/C9ffDqFGWfP3yy3DttRAVFe4mi4iISA5RgC1yBJKSrLf66adh40bLt378cWgYtRQeexKGDoXoaIu2lV8tIiJSKCjAFjkMW7fCm29aj/X69dC5s6WGtC672KLtYcMgMtLq8N1/v8rtiYiIFCIKsEUOwcaNNp35q69CQoIF1vffD6dUXgBPPmkzxxQpAtddBwMHQrVq4W6yiIiI5DIF2CLZsHq19Va/9Rbs2AHdu9vgxTbFZ1tg/cknlgpyyy02pXnVquFusoiIiITJQcv0OeeGOOfWO+dmZ1n3iHNulXNuRrCclWXbvc65Rc65Bc65LlnWnxmsW+ScG5hlfR3n3KRg/SfOuWLB+qjg90XB9tohe9Yi2bR0qXVG16lj4xO7d7dpzj9/+B/aPNMTmjWDr7+Ge+6BuDgb6ajgWkREpFDLTh3socCZ+1j/kve+ZbCMA3DONQYuApoE93nDORfpnIsEXge6Ao2Bi4N9AZ4NjnU0sBnoH6zvD2wO1r8U7CeSK/79F/r2hfr1YcgQq6j3778w/O5ZNHnoAmjZEn74AR580ALrp5+GSpXC3WwRERHJAw4aYHvvfwU2ZfN43YAR3vtd3vulwCLg+GBZ5L1f4r1PBkYA3ZxzDjgNGBXcfxhwfpZjDQtujwI6BfuL5JgFC+Dyy6FRI/j0U6tjvWQJvHXzXOre2xuaN7fA+pFHLLB+7DGoUCHczRYREZE85EhmcrzROTczSCEpF6yrDqzIss/KYN3+1lcAErz3qXus3+1YwfYtwf4iITd/Plx2GTRuDJ9/DrffbukhLw6YT/W7LoGmTWHcOHjgAQusH34YypYNd7NFREQkDzrcAPtNoB7QElgDvBCqBh0O59wA59xU59zUDRs2hLMpks/Mnw+XXGKB9ejRVqp66VJ4bsBCKt95uc11/uWXVhEkLs6KXJcrd9DjioiISOF1WFVEvPfrMm475wYDXwe/rgJqZNk1NljHftZvBMo654oEvdRZ98841krnXBGgTLD/vtrzDvAOQJs2bfzhPCcpXJYtsyyPDz6w4h93323BdaVtS+Cex2H4cJvG/I474K67lF8tIiIi2XZYPdjOuaxlEroDGRVGvgQuCiqA1AHqA5OBKUD9oGJIMWwg5Jfeew/8BPQM7t8X+CLLsfoGt3sCPwb7ixy2tWstr7p+ffj4Y7j1VuuxfubaOCrdexU0aAAjRthOS5fC//6n4FpEREQOyUF7sJ1zHwMdgYrOuZXAw0BH51xLwANxwDUA3vs5zrmRwFwgFbjBe58WHOdGYDwQCQzx3s8JHuIeYIRz7gngb+C9YP17wHDn3CJskOVFR/pkpfDauhWee85qWe/aBVdeaQVAavjl8OBT8N57EBEB11+vCWJERETkiLiC1incpk0bP3Xq1HA3Q/KIXbtsSvMnnrBZGHv3tjTq+jGrbG7zd98F7+Hqq+HeeyE2NtxNFhERkXzCOTfNe99mz/WayVEKpLQ0m7X8wQct3/r00+GZZ6B11dV24513bKcrr7S5zmvWDHeTRUREpIA4kjJ9InmO91ZN79hjoU8fKF8evvsOvh++ltbDb4V69eCNN6wm37//wttvK7gWERGRkFIPthQIyckwcqTNVD5jBtSta4MYe7VeTMSrr0C3d22nPn2slnXduuFusoiIiBRQCrAlX1u92rI93n7bKoQ0agSD3/H0rf8HRQe9AJeMgSJF4NJLLRXk6KPD3WQREREp4BRgS77jPUycCK+/brMupqbCWWfBLf23c/qmkUQMfhsmT7YJYe69F264QVVBREREJNcowJZ8Y+1amxhmyBBYsMBmKr/5Js+t7SdTY/y70HcEbN8ODRta9N23L5QoEe5mi4iISCGjAFvytJQU+OYbK1M9dqwV/jjxRHjwunh67vo/oj54F16aAzExVoPvqqugXTtwLtxNFxERkUJKAbbkSfPnw/vvW4/12rVQuTLceXs61x8zgZrfvQt3j7FBi8cfb0nYvXtD6dLhbraIiIiIAmzJO7ZutUogQ4bAn39CZCScfTbccN4KOi1/n8hhQ6yodfnycN110L8/NGsW7maLiIiI7EYBtoSV9/DrrxZUjxoFiYlWCeSlp5LoV3YMZUe/D1f/YDuecQY8+yycfz5ERYW76SIiIiL7pABbwmLVKhg61NJAFi+GUqXgsks9N7WdTJPJ7+OeHQFbtkCtWvDQQzZgsU6dcDdbRERE5KAUYEuuSU6Gr76yAYvjx0N6OnTsCE/fspZuW4dT7KOhMHguREfDBRfAFVfYDhGacFRERETyDwXYkqO8h1mzYNgwGD4cNmyA6tXhsVs3cfVRX3DUr6PgtvFWHqR9exg8GHr10oBFERERybcUYEvIJSfDb79ZWb0xY2DJEihaFC7rvJ7b646hyfzPcIN+tBliatWCu+6Cfv3gmGPC3XQRERGRI6YAW0JizRqrVz12LHz/PWzbBsWKwaUnLOaD48Zx3KrRFPvmF8sLOfpouPNO6NkTjj1WNatFRESkQFGALYclLQ2mTIFx4yyonj7d1teptotHT/qV84qMo868cUT8+q9taNgQ7r/fgupmzRRUi4iISIGlAFuybdMm+O47C6i//Rbi42384fmtlvHEed/Qfss4Sk+ZgPsm0cronXoq3HwjdO1qvdYiIiIihYACbNkv72HOHAuov/4a/vjDMjyOKp/Kzcf+wQXRY2nw79cUmTYXpgG1a1vlj65dLbiOiQn3UxARERHJdQqwZTdJSfDTT/8F1cuX2/oOTTcy4txvOTVxLBWmfIP7IQGKFIFTToEB/eGss2yQolI/REREpJBTgC3Ex1sw/eWXVp86MRFioj1Xtp3D5a2/psWqsURN/QNmp0OlSjaT4jnn2MyKKqcnIiIishsF2IXUokXwxRe2/P67pX40qbqJF06ZSNci31Fz5ljcz8ts51atbIDiOedAmzaa+EVERETkABRgFwKpqTBzpuVQZyzLlkFVVtO75l882/oXWiT8QvSimbg13nKnTz8dHrjfUj+qVw/3UxARERHJNxRgFzBbt1ow/c8//y2zZnrK7FxLc2Zyaqlp3FZqMo3KTKHkltWwHNgQbbMoXv6oTU1+/PFWBUREREREDpkC7HwqPd1mSJw1a/dges3SJBozl+bM5PiomVwbPZMGbiYlibc7bgOq1P8vkD7uOGjdWgG1iIiISIgowM7jvLdZEmfPtmB69mxbFs9OotbO+TRhDk2Yy40l59CEOVR2S3De230jonH1m0LzbtC8uU3w0rIllCsX3iclIiIiUoApwM5DNm/+L4CePdtSO5bP2kLZLXE0Zi5NmEPvqDk8ETGHqruWEEE6AL5IEVyN+tCkFTS5DJo2hebNcfXqQWRkmJ+ViIiISOGiADsHpKZaLnRCAmzZYj8zli1bIGXlOtLWbsDHbyQ9fhPp8Rth4yaKbd9IBTZSi9V0iFhOLbecEmnbMo/rIyNxdepDkxbQ5BJo0gSaNMHVrw/FioXp2YqIiIhIVgqwQ+DLL62KXUYAvW3bgff/i/Noy+S91qdGFiO1TAUiqlWl6NH1cTU7Qc2atjRsiGvQQLnSIiIiInmcAuwQKFMG6teHsmVtKVPmv9t7/l6mDJT+7WHYuQPKl4cKFTJ/FomJoYhmQhQRERHJ1xRgh0CHDrZk27ln5VhbRERERCS8NCWfiIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhNBBA2zn3BDn3Hrn3Ows68o75753zi0MfpYL1jvn3CDn3CLn3Ezn3LFZ7tM32H+hc65vlvWtnXOzgvsMcs65Az2GiIiIiEhelp0e7KHAmXusGwhM8N7XByYEvwN0BeoHywDgTbBgGXgYaAscDzycJWB+E7g6y/3OPMhjiIiIiIjkWQcNsL33vwKb9ljdDRgW3B4GnJ9l/Qfe/AWUdc5VBboA33vvN3nvNwPfA2cG20p77//y3nvggz2Ota/HEBERERHJsw43B7uy935NcHstUDm4XR1YkWW/lcG6A61fuY/1B3qMvTjnBjjnpjrnpm7YsOEwno6IiIiISGgc8SDHoOfZh6Ath/0Y3vt3vPdtvPdtKlWqlJNNERERERE5oMMNsNcF6R0EP9cH61cBNbLsFxusO9D62H2sP9BjiIiIiIjkWYcbYH8JZFQC6Qt8kWV9n6CayAnAliDNYzzQ2TlXLhjc2BkYH2zb6pw7Iage0mePY+3rMURERERE8qwiB9vBOfcx0BGo6JxbiVUDeQYY6ZzrDywDegW7jwPOAhYBicAVAN77Tc65x4EpwX6Pee8zBk5ej1UqiQa+CRYO8BgiIiIiInmWs/TmgqNNmzZ+6tSp4W6GiIiIiBRwzrlp3vs2e67XTI4iIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJISOKMB2zsU552Y552Y456YG68o75753zi0MfpYL1jvn3CDn3CLn3Ezn3LFZjtM32H+hc65vlvWtg+MvCu7rjqS9IiIiIiI5LRQ92Kd671t679sEvw8EJnjv6wMTgt8BugL1g2UA8CZYQA48DLQFjgcezgjKg32uznK/M0PQXhERERGRHJMTKSLdgGHB7WHA+VnWf+DNX0BZ51xVoAvwvfd+k/d+M/A9cGawrbT3/i/vvQc+yHIsEREREZE86UgDbA9855yb5pwbEKyr7L1fE9xeC1QOblcHVmS578pg3YHWr9zHehERERGRPKvIEd7/JO/9KufcUcD3zrn5WTd6771zzh/hYxxUENwPAKhZs2ZOP5yIiIiIyH4dUQ+2935V8HM9MBrLoV4XpHcQ/Fwf7L4KqJHl7rHBugOtj93H+n214x3vfRvvfZtKlSodyVMSERERETkihx1gO+dKOOdKZdwGOgOzgS+BjEogfYEvgttfAn2CaiInAFuCVJLxQGfnXLlgcGNnYHywbatz7oSgekifLMcSEREREcmTjiRFpDIwOqicVwT4yHv/rXNuCjDSOdcfWAb0CvYfB5wFLAISgSsAvPebnHOPA1OC/R7z3m8Kbl8PDAWigW+CRUREREQkz3JWoKPgaNOmjZ86dWq4myEiIiIiBZxzblqWUtWZNJOjiIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCqEi4GyAikhM2bdrE4sWLWb16NfHx8ZnLpk2bSEtLIyIiAucczjmKFStGuXLlKF++/F5LTEwM6enpuy07d+4kKSkpc0lJSaFYsWJERUVl/ixdujRVq1alTJkyOOfC/XKIiEguUoAtInmS956lS5eyfPly1q5dy9q1a1m3bh3x8fHs3LmT5OTkzAUgOjoagKVLl7JkyRISEhL2Ombx4sWpUKECkZGReO9JT0/He8+uXbvYvHkz6enpIX8exYsXp0qVKlSrVo06depQv359mjVrxgknnEC1atVC/nh5XXp6OuvXr2flypWsXr2adevWsWHDhsxl8+bNmee3SJEiREVFUaZMGapWrUqVKlWoWrXqbkv58uWJiDi8i7GpqakAFCmij0IRCS3nvQ93G0KqTZs2furUqeFuhkiBkJKSQkJCAgkJCWzZsoWtW7eydetWtm3bxvbt24mIiKBo0aK7LUWKFMlcIiMjSUlJYefOnZnLrl27Mm8nJSWRmJi415KQkMCsWbPYsmXLbu0pUqQIFStWJDo6erceY+89SUlJpKenU7t2berWrUu9evWoV68esbGxVKpUiYoVKxITE7Pf55qens62bdvYvHkzmzZtylwSExOJjIzM7PGOiIggKiqK6OhoYmJiiI6OpmjRopnB/q5du9i1axcJCQmsWbOGtWvXsmbNGlavXs3ixYtZsWIFGe+7sbGxtG3blrZt29KqVStatGhBxYoV822Pd1JSEqtXr2bVqlW7LStXrsy8vXr16szANquSJUty1FFHUb58eYoXL07RokVJTU1l586dma/l9u3b97pf0aJFqVy5cmYAXqVKFUqVKkV0dDTOOVJTU9mxYwcbN27ca9m6dSvOOSpVqkRsbCytWrWiTZs2nHbaaTRo0CA3XjLJhp07d7J48WLmz5/PokWLWL9+PfHx8ezatYv09HSKFy9OxYoVd1uqV69ObGwsVapUITIyMtxP4ZB470lMTGTLli0kJiZmvp9mvL/GxMRQvHjxkL5PpKWlsX37drZt28a2bdtITEzM7IDIaFPWZV/rsq5PT0/f7f19X+/1Ge/Zh/P6bN26dbf36UqVKjFlypSQvR6Hwjk3zXvfZq/1eT3Ads6dCbwCRALveu+fOdD+CrDzvowew4xAa8+fycnJu/2jZvzc37rU1FSSkpJ2C9oybm/fvj0zKNxzSUtL2+24QOabWMYb2b5+ZtwGe1NKTU0lNTU18/bBfmYcp1ixYhQrVizzdnbXpaWlZb5B7dixY683rKzLrl27MoPCjCXj94z0iH3JCKwTExNz7O8AwDmXGahmLNHR0ZQqVYomTZrQqlUrjj76aCpXrkyVKlUoV65cvg0+MyQlJfHPP/8wadKkzGXJkiWZ2zOChUqVKlGpUiUqVKhATEwMJUqUoFatWjRs2JCGDRtSq1atXA0cvPfEx8ezZMkSlixZwtKlS1m6dOluwfOmTZv2ul+JEiWIjY3NDHiqV6++2+3KlStTqVIlihcvftA2bN++nTVr1uz2xWXPn+vWrWP79u0kJSUB9j8dHR1NhQoV9rmkpaWxdu1a4uLimDZtGhs3bgSgQYMGnHPOOZxzzjmcdNJJFC1aNNuvVUpKCitWrGDz5s27rY+IiODoo4+mVKlSu61PTk5m2bJlma/p2rVr2bx5c+ayadOmzNs7d+4kPT2dYsWKUbp0acqVK0eNGjWoVavWbkvt2rXz1P9LWloaKSkpJCcn7/U+tef79MaNG1myZAmLFy9m8eLFrFq1arf36ZiYGCpWrEhUVBSRkZHs3LmT+Pj4fX4BK1KkCNWqVSM2NpYaNWpQvXp1ypYtS8mSJSlZsiQlSpTY7Qty1i/JWT9LkpOTd3sfzehciIqKonjx4pk/M25HRETsllaWEcDu+Tm0bds2tmzZktmBkXE7LS3tgK9n0aJFKVOmzF5LdHT0bm0EdvtczPozKSkpM6DO+H/JDUWLFiU6Opro6OjDfg8rXbr0bql8tWvX5tFHHw1xS7MnXwbYzrlI4F/gDGAlMAW42Hs/d3/3KQgBdnp6OomJiWzfvn2vJWtQuGegmfFPvOfPrLcz3jT2/FaZlJTEjh07dlt27twJkBmIZQ3IDhScZcgaSGcNqFNSUnL2BcwiIiKC0qVL77WUKlUq8wMz47lkBOspKSl7/dzXOiCzlzZrj+2ev+/5My0tjeTk5MwPm6y3s7MuIiKCEiVK7BaUZgSmGT8zlqioqN3+PjKWjN/3JzIyknLlylG2bNndllKlSmW+hiVKlCA9PT3z9clYsn7pSE1NpVixYpkfPFk/gDJ+5pUAIJzi4+P5559/mDlzJqtXr85Ml4iPj2fjxo27fRBmiIqKokGDBjRv3pzmzZvTokULmjdvTpUqVQ7pNc36BTUpKYn169ezbNmyzCUuLi4z7WbHjh273bdy5cqZQUv16tWpVq1a5u2MpXTp0vnmHHvvWbJkCd988w1ff/01P/30E8nJyZQqVYr27dtz4okn0qhRI+rWrUvdunUpW7Ys6enpTJ8+nZ9++ompU6cyffp0li5dut8AyTlHo0aNOOqoo0hISCA+Pn6vABKgVKlSlC9fnnLlyu22xMTE4JwjOTmZLVu2sGnTJpYvX86yZcv2+kJcvHhxSpcuTcmSJSlVqhSlSpXKDCwz1mX9Pev6jP/zMmXKZL5nZqRWrV+/nri4uMy/j4zHj4+Pz/zSn/Fz165dpKSkHHJPZeXKlTOvQtWrV4+jjz6aRo0a0aBBA0qWLLnP++zcuZONGzeyfv16Vq9ezYoVK1ixYgUrV67MvL1q1apDCiaLFi2aeUVlz/fRjPfkQ5VxXjJe51KlSu0zWC5TpgwxMTGZX04yPnt27NiRGYjvuWT06md9ry9evDjR0dF7/YyOjt7tbyPr30iJEiX26og5UCyw57qIiIi9Ok+yXvErSPJrgN0OeMR73yX4/V4A7/3T+7tPfgiwL7/8cqZPn77fICojsM1pWf/4o6OjM4O2EiVKUKJEicwepf0F9NkRFRW1WzCVndtFixbN/Oa9v6A+68+MN8Csbx5Zl/zy4Z4dGa97QXpOkn3x8fEsWLCABQsWMH/+fObOncvMmTNZsWJF5j4xMTGZucmpqakH/QK3r3SNDKVKlaJWrVrUqVMnM6isW7cuderUoXbt2pQoUSI3nnbYbN++nR9++IHvvvuOiRMnMnv27N22lytXjsjISOLj4wGoVasWrVu3plGjRtSrV48KFSrs9r+anJzM7NmzmTx5Mlu3bs0cWFu7dm3q1KmTuVStWvWQ88K992zcuHG3L0erV6/O/GKWcfk/a4dNxraD9ZZmKFmyZGbazp6vQ82aNalUqVLm50fG50nGe3rWq3IZwV3GZ0/JkiV36wApW7Zs5piKnJARpGa8DikpKZmfQxlXDDM6KA7Ww5qenr7PK7Le+8xe5IwUs4xgtlixYjn23CT35dcAuydwpvf+quD3y4G23vsb99hvADAAoGbNmq2XLVuW6209FPfeey8LFy7cZzpA0aJFKVGixD57FUqUKJH5pru/b4wZPahZ/6mzrouKisqR/C0RCZ9NmzYxa9Ys/vnnH+Li4lizZg0JCQl7BTb7+pn1ikfGINDatWtTq1YtypYtq/eJLLZt25aZIpOxJCYmctppp9G5c2cqV64c7iYeMu89ycnJuwXeGakLGSkLWVManHOZfx8ZP0uXLh3upyESNgU6wM4qP/Rgi4iIiEj+t78AO69PNLMKqJHl99hgnYiIiIhInpTXA+wpQH3nXB3nXDHgIuDLMLdJRERERGS/8nR1fe99qnPuRmA8VqZviPd+TpibJSIiIiKyX3k6wAbw3o8DxoW7HSIiIiIi2ZHXU0RERERERPIVBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERCSAG2iIiIiEgIKcAWEREREQkh570PdxtCyjm3AViWQ4evCMTn0LElZ+nc5V86d/mXzl3+pPOWf+nc5b5a3vtKe64scAF2TnLOTfXetwl3O+TQ6dzlXzp3+ZfOXf6k85Z/6dzlHUoREREREREJIQXYIiIiIiIhpAD70LwT7gbIYdO5y7907vIvnbv8Sect/9K5yyOUgy0iIiIiEkLqwRYRERERCaFCH2A754Y459Y752ZnWdfCOfenc26Wc+4r51zpYH0x59z7wfp/nHMds9ynmHPuHefcv865+c65C3L/2RQezrkazrmfnHNznXNznHO3BOvLO+e+d84tDH6WC9Y759wg59wi59xM59yxWY7VN9h/oXOub7ieU2ERynMXbC/tnFvpnHstHM+nMAnx/93/gmPMC/Zx4XpeBd1hnLeGwWfgLufcnQc7juScUJ27YFtZ59yoIEaZ55xrF47nVFgU+gAbGAqcuce6d4GB3vtmwGjgrmD91QDB+jOAF5xzGa/h/cB6730DoDHwSw63u7BLBe7w3jcGTgBucM41BgYCE7z39YEJwe8AXYH6wTIAeBPsTQp4GGgLHA88nPFGJTkmJOcui8eBX3Oj4RKy/7v2wIlAc6ApcBzQIRefR2FzqOdtE3Az8Hw2jyM5J1TnDuAV4FvvfUOgBTAvpxtfmBX6ANt7/yv2B5lVA/77wP4eyOiNbgz8GNxvPZAAZNSbvBJ4OtiW7r1Xofcc5L1f472fHtzehr1RVAe6AcOC3YYB5we3uwEfePMXUNY5VxXoAnzvvd/kvd+Mne89v3BJCIXw3OGcaw1UBr7LvWdQeIXw3HmgOFAMiAKKAuty63kUNod63rz36733U4CUbB5Hckiozp1zrgxwCvBesF+y9z4hF55CoVXoA+z9mIP98QJcCNQIbv8DnOecK+KcqwO0Bmo458oG2x93zk13zn3qnKucqy0uxJxztYFWwCSgsvd+TbBpLRZ8gb0hrchyt5XBuv2tl1xwJOcuuHr0ArDbZVDJHUdy7rz3fwI/AWuCZbz3Xr1puSCb5+1QjyO54AjPXR1gA/C+c+5v59y7zrkSOdZYUYC9H1cC1zvnpgGlgORg/RDsA2Iq8DLwB5AGFAFigT+898cCf7LvyzMSYs65ksBnwK3e+61Zt3krkaMyOXlUCM7d9cA47/3KHGqi7MeRnjvn3NFAI+x9szpwmnPu5BxqrgRC9X55oONIzgjBuSsCHAu86b1vBezgv7QSyQEKsPfBez/fe9/Ze98a+BhYHKxP9d7f5r1v6b3vBpQF/gU2AonA58EhPsX+kCUHOeeKYm84H3rvM177dVnSB6oC64P1q/jvSgTYB/uqA6yXHBSic9cOuNE5F4d9oe3jnHsmF5pfqIXo3HUH/vLeb/febwe+wc6n5JBDPG+HehzJQSE6dyuBld77jCsOo1CckqMUYO+Dc+6o4GcE8ADwVvB7TMYlFefcGUCq935u8O3xK6BjcIhOwNzcbndhElQceA+Y571/McumL4GMSiB9gS+yrO8TVDU4AdgSXF4bD3R2zpULBjd2DtZJDgnVufPeX+q9r+m9r42liXzgvVePTA4K4f/dcqBDkG5XFBvgqBSRHHIY5+1QjyM5JFTnznu/FljhnDsmWKU4JYcV+olmnHMfY4FxRWyQzcNASeCGYJfPgXu99z7IfxoPpGO9MP2998uC49QChmO92huAK7z3y3PtiRQyzrmTgInALOx8ANyH5aaNBGoCy4Be3vtNwZvUa9gAxkTs/EwNjnVlcF+AJ7337+faEymEQnnushyzH9DGe39jrjyJQipU5845Fwm8gQ268lhlg9tz9ckUIodx3qpgqZClg/23Y4P8m+/rON77cbn0VAqdUJ077/1W51xLrEpaMWAJ9v+4ORefTqFS6ANsEREREZFQUoqIiIiIiEgIKcAWEREREQkhBdgiIiIiIiGkAFtEREREJIQUYIuIiIiIhJACbBERERGREFKALSIiIiISQgqwRURERERC6P8BdbnQhAYvE50AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test: \n",
      "\n",
      "Test Statistic                  -1.914667\n",
      "p-value                          0.325193\n",
      "#Lags Used                       2.000000\n",
      "Number of Observations Used    262.000000\n",
      "Critical Value (1%)             -3.455558\n",
      "Critical Value (5%)             -2.872636\n",
      "Critical Value (10%)            -2.572683\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Grouping our data into months\n",
    "\n",
    "monthly_data = new_data.resample('MS').mean()['value']\n",
    "\n",
    "# Checking the stationarity of our series\n",
    "\n",
    "stationarity_check(monthly_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2101d8a1",
   "metadata": {},
   "source": [
    "Though the variation in standard deviation is small, the mean is increasing with time and thus, this is not a stationary series.The pvalue is greater than 0.05 hence we fail to reject the null hypothesis that the series is non-stationary. All the tests show that our series is not stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fd18a6d",
   "metadata": {},
   "source": [
    "#### Time Series Decomposition\n",
    "\n",
    "Now that we know that our data is not stationary,we will allso investigate its pattern in the past which will come in handy when forecasting future house values. We break it into three components to better see its trend, seasonality and residuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a91a754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import and apply seasonal_decompose()\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "decomposition = seasonal_decompose(np.log(monthly_data))\n",
    "\n",
    "# Gather the trend, seasonality, and residuals \n",
    "trend = decomposition.trend\n",
    "seasonal = decomposition.seasonal\n",
    "residual = decomposition.resid\n",
    "\n",
    "# Plot gathered statistics\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(411)\n",
    "plt.plot(np.log(monthly_data), label='Original', color='blue')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label='Trend', color='blue')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal,label='Seasonality', color='blue')\n",
    "plt.legend(loc='best')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label='Residuals', color='blue')\n",
    "plt.legend(loc='best')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49931376",
   "metadata": {},
   "source": [
    "This makes it easier to identify a changing mean or variation in our data. From the decomposition plot it clearly shows an upward trend in our series with seasonality and minimal variation.We will need to detrend our data because if seasonality and trend are part of the time series then there will be effects in the forecast value. As the pattern of the forecasted time series can be different from the older time series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94579734",
   "metadata": {},
   "source": [
    "#### Detrending the Series\n",
    "We decided to use differencing as a method of detrending the data because differencing is similar to the derivative of a function and more powerful than the adjustment through regression and seasonal means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2bebbae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1998-04-01     9394.335394\n",
       "1998-05-01     9917.679821\n",
       "1998-06-01    10441.734022\n",
       "1998-07-01    10964.776201\n",
       "1998-08-01    11481.274197\n",
       "Freq: MS, Name: value, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Differencing to remove trends\n",
    "\n",
    "data_diff = monthly_data.diff(periods=24).dropna()\n",
    "data_diff.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2ed9c256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test: \n",
      "\n",
      "Test Statistic                  -3.109067\n",
      "p-value                          0.025889\n",
      "#Lags Used                       2.000000\n",
      "Number of Observations Used    238.000000\n",
      "Critical Value (1%)             -3.458128\n",
      "Critical Value (5%)             -2.873762\n",
      "Critical Value (10%)            -2.573283\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Rechecking stationarity\n",
    "\n",
    "stationarity_check(data_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d18620",
   "metadata": {},
   "source": [
    "The p-value is now below 0.05 therefore we reject the null hypothesis that the series is non-stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d52045",
   "metadata": {},
   "source": [
    "#### Auto-Correlation and Partial Auto-Correlation of the Differenced Data\n",
    "\n",
    "Plotting the ACF and PACF functions help determine the AR and MA components in the models we intend to build. These functions consider all the above components of a time series like the trend and the seasonality and find their correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5befa237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAADSCAYAAACxSkJSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAAsTAAALEwEAmpwYAAAmiklEQVR4nO3df5xcdX3v8dd7d7P5DUkgYMgPgoIKVA12Ba22RfwFXgvWaynYauiFm/p4iP11bQHtRaVqsfdhUXu9vXIFoWr5UeqP3BaLCHLtfVQtASIQuJiIQBJCEvJrs7/m1/ncP86ZzexmZneTmd2Z2X0/H495zJzv+Z6Z78kkec/3e77nHEUEZmZm1p46mt0AMzMzO3oOcjMzszbmIDczM2tjDnIzM7M25iA3MzNrYw5yMzOzNuYgN7NJI+kySf+3ju2/K2ltI9tkNt04yM2aTNIDkvZJmn0E24SkUyezXVNN0ickfb2yLCIuiIhbm9Ums3bgIDdrIkmrgV8FAriwua0Zm6SuiZSZ2dRykJs11weAHwO3AMNDyFkv/YqK5eEhakk/zIp/KqlP0m9n5f9Z0hZJeyWtl3RSxfZnSro3W7dT0kez8tmSPi/p+ezx+fLIgKRzJW2TdJWkF4CvZr3muyR9XVIvcJmkYyXdJGmHpO2SPiWps9rOSvqCpK2SeiU9JOlXs/LzgY8Cv53t009H/zlI6pD055KelbRL0t9JOjZbtzobpVgr6TlJL0r6WN3fjlkbcJCbNdcHgG9kj3dIOnG8DSLi17KXr4mIBRFxh6TzgL8ELgaWAc8CtwNIWgh8H/gX4CTgVOC+7D0+BrweWAO8Bjgb+POKj3sJsAQ4GViXlV0E3AUsytp9C1DM3vcs4O3AFVT3YPZZS4C/B/5B0pyI+BfgM8Ad2T69psq2l2WPNwMvBRYA/31UnTcBrwDeAlwr6fQa7TCbNhzkZk0i6U2kAXlnRDwE/Bx431G+3e8AN0fEwxGRA64B3pAN3b8LeCEiPhcRQxFxMCJ+UrHddRGxKyJ2A58E3l/xvgnw8YjIRcRgVvajiPh2RCTAMcA7gT+KiP6I2AXcAFxSrZER8fWI2BMRxYj4HDCbNHgnuo9/HRFPR0Rfto+XjBre/2REDEbET4Gfkv44MZvWHORmzbMW+F5EvJgt/z0Vw+tH6CTSXjgAWdDtAZYDK0l/JIy7Xfb6pIrl3RExNGqbrRWvTwZmATsk7Ze0H/gycEK1D5P0EUlPSjqQ1T0WOH7sXRuzrV1A5SjGCxWvB0h77WbTmieqmDWBpLmkw+Cd2fFnSHuniyS9BugH5lVs8pJx3vJ50lAtv/984DhgO2nwVu0hV2y3KVtelZWVVbs9YmXZViAHHB8RxbEamB0P/zPSYe9NEZFI2gdojM+q1tayVaRD+juBFeNsazZtuUdu1hzvBkrAGaTHjNcApwP/SnrcfCPwHknzstPMLh+1/U7S48RltwG/J2lNNlntM8BPIuIZ4J+AZZL+KJvctlDSORXb/bmkpZKOB64FRpwCNpaI2AF8D/icpGOyCWkvk/TrVaovJA3e3UCXpGtJh+Yr92m1pFr/L90G/LGkUyQt4NAx9TF/QJhNdw5ys+ZYC3w1Ip6LiBfKD9LJW79Depw5Txput5JOKqv0CeDWbDj74oj4PvBfgX8EdgAvI+uFR8RB4G3Ab5AOPW8mnTAG8ClgA/Ao8BjwcFZ2JD4AdANPAPtIJ8Itq1LvHtIJdz8jHRYfYuQw/T9kz3skPVxl+5uBrwE/BH6Rbf/hI2yr2bSjiPFGs8zMzKxVuUduZmbWxhoS5JJuzi7Q8HiN9ZL0xexiFY9Kem3FurWSNmcPX1PZzMzsCDSqR34LcP4Y6y8ATsse64C/BZC0BPg4cA7phSg+Lmlxg9pkZmY27TUkyCPih8DeMapcBPxdpH5MeorNMuAdwL0RsTci9gH3MvYPAjMzM6swVcfIlzNyduq2rKxWuZmZmU1A21wQRtI6sms9z58//5df+cpX1vV+uw7m2Nk7+oJVcOIxczhh4exJq3dwqMBzewdJKs4W6JBYtWQuC+fMGrHtL17sZyBfIomgQ2JedyenHD+/6v4cHCowWEiYO6vjsPcxM7P29tBDD70YEUurrZuqIN9OepnIshVZ2Xbg3FHlD1R7g4i4EbgRoKenJzZs2FBXg+57cicfvu0RBvKl4bJ53Z38zaVn8ZbTT5y0el+8bzM33PuzEZewEnDl217Oh99y2rjv99lR71dKgvff9BM2bt1PZ75EZ3cnL1+5iK9dfg6dHaJSKQkeeGoXm57v5cyTjuHcV5xwWB0zM2s9kp6ttW6qhtbXAx/IZq+/HjiQXRHqHuDtkhZnk9zenpVNunNfcQJrVi6inGPzujtZs3IR577ihEmtd+ZJxzC3e+QdHud2d3LGSceMKNv0fC+DFSEOMJgv8cTzvSPKHnhqFxu37mcgXyKAgXyJjVv388BTu0bUKwf+h297hBvu/Rkfvu0R3n/TTyglh19HoJQE9z25ky/et5n7ntxZtY6ZmbWGhvTIJd1G2rM+XtI20pnoswAi4n8Cd5PeIWkL6Y0Mfi9bt1fSX5De2hDSuzCNNWmuYTo7xNcuP4cLvvBDBnIlPnnRmVV7qI2uVw78Hz+9hyTGD/zKHvmRBn5lz70y8GFk4Nfq4Q/mS8zN2leth29mZs3XkCCPiEvHWR/Ah2qsu5n00otTrrNDLJ7XzeJ5jAizyaw3XQIfPFRvZtYK2may23QyHQL/SHruDnwzs8njIG9hrRz4Hqo3M2sNvtb6NFAO/OWL5/KW00+sGpDlwD/1hAWsWDSXv7n0rKphOtFJe42ejAeeZGdmdjTcI59BGtnDb+ZQvZmZHeIeuR1mIj38Rp9u5567mdnRcY/cjkqjj827525mdnTcI7ej1shj8+65m5kdHffIbdJN5Ni8e+5mZkfHPXJrCc3suZuZtTMHubWMRk6ym+jpceAheDNrbx5at7bS6NPjPARvZu3OPXJrO43suXvynJm1u0bd/ex84AtAJ/CViLh+1PobgDdni/OAEyJiUbauBDyWrXsuIi5sRJtsZptoz92T58ys3dUd5JI6gS8BbwO2AQ9KWh8RT5TrRMQfV9T/MHBWxVsMRsSaetthNtpEZss3+try4JvEmNnUakSP/GxgS0Q8DSDpduAi4Ika9S8lvV+5WdP5tDcza3eNOEa+HNhasbwtKzuMpJOBU4D7K4rnSNog6ceS3l3rQySty+pt2L17dwOabebT3sys/U31ZLdLgLsiorJrc3JE9ADvAz4v6WXVNoyIGyOiJyJ6li5dOhVttRnCp72ZWTtrxND6dmBlxfKKrKyaS4APVRZExPbs+WlJD5AeP/95A9pl1jA+7c0mIiKIgACS4dfpc7o+XU4iq5uVMboeI9cH2ZsOr8vKyttXbFNZNnJ9VCk71O7hdSP2Z8TejdrXKvtf7Q+lRt3RbZpOOiVOO3HhlH1eI4L8QeA0SaeQBvglpL3rESS9ElgM/KiibDEwEBE5SccDbwT+qgFtMmu4Rl5q1pPnGitJgiSCUhaeSQSl5FBgJpH+OZZfl8M0SQ7VT2Jk/SBIkkMBm1S893DQBoeHss14XZ1T+++z7iCPiKKkK4F7SE8/uzkiNkm6DtgQEeuzqpcAt0eM+Kt+OvBlSQnpMP/1lbPdzdqNT3sbWymJQ49y2GavkxFlHHqdhWy5vBzYSXIooM1msoacRx4RdwN3jyq7dtTyJ6ps92/AqxrRBrNW0azT3iZbkgSFJKGUBMUkKJWy5yQoZuWl4eXDA7uUhHusZpPAl2g1a4JGn/YGEx+CLwdysRQUS4fCuVAqP6fBXBwO6vS1O75mrclBbtYEjZw8lyTBULHE7331QR7ddoChQok5szo5fdlC/vI9ryYICsVD4e2haLPpxUFu1iTjDcEXSgmvW72EX1p+DBue2UcSMGdWB684cSFLF85m49b9FEppOD/87D42bt1PrpgAMFgosen5Xr7/xE5ee/Liqd41M5tCDnKzKZYkQb6UkCsmFEoJEfCLF/splBLyxYR8KaFQTIaHsv/wvJdz1TcfJVcocdmvnMKalYvoz40cbn9mTz/5LMTL8sWEZ/b0HxbkSRJs3LqfZ/b0s/q4+en58W04cc7MUg5yswaKSEO6PNt6+/7BNJyzR65YolA6NLQ9WEgD+YUDQzXfs6NDLJzTxcI5XTV716uPm093V8dwjxygu6uD1cfNH1EvSYLPfPdJtuzqI19M6O7q4NQTFvDRC053mJu1KQe52RGKCHqHCuQKaTDnisnw63zWk+7PFwF4bs/AlLRpzcpFnHrCAp7Y0UsEzM4Ces3KRSPqbdy6ny27+oYDP1dM2LKrj41b91f9keDeu1nrc5CbjZIkQa6YMFQoMVQskSskDBVLDBUSDg4VCGDT9sMvudpMHR3ioxecftgQ/OjQPdIhePfezVqfg9xmpIg0rAfzJQYLJYYKJZIIHn5uH/liMsYlJVtXI4fg4ch772bWHA5ym9aCtGe5q3coC+xkOLgrwzpfysKqkFR/o2liokPw4Al0Zu3CQW7TQqGUMJBPA3ogX2IgX2SoUOLgUAGAn+/ub3ILW8NEh+DBE+jM2oWD3NpKKQkG8sXhq5Btej69AEq+2MqD3q1lIkPwMHkT6MyssRpyP3JJ50t6StIWSVdXWX+ZpN2SNmaPKyrWrZW0OXusbUR7bHoYKpTY259n274BfrbzII88t48Hn9nL49t7GSyUyJcSegeLDvFJUu69L180l6ULuvmD806r2sseawh+tCRJL17zzYe38fCz+0h8lTmzutXdI5fUCXwJeBuwDXhQ0voqdzG7IyKuHLXtEuDjQA/p4cyHsm331dsuax8RwVAhoS9XHJ509uAzeymW/J98s/kcdrPW14ih9bOBLRHxNICk24GLgIncjvQdwL0RsTfb9l7gfOC2BrTLWtRgvkRfrkh/rkhfrshAvjR8/e/ypDOHePvwELxZczUiyJcDWyuWtwHnVKn3HyX9GvAz4I8jYmuNbZdX+xBJ64B1AKtWrWpAs20q5ItpTztXTCglCf/+i72+acc0M1nnsHsWvNnETNVkt/8N3BYROUm/D9wKnHckbxARNwI3AvT09DgJWlBE0Jf1svuGihzMFYdP58oV00uROsSnJw/BmzVPI4J8O7CyYnlFVjYsIvZULH4F+KuKbc8dte0DDWiTTYFCKRm+X/Xj2w/Qnyv6ntVW02QMwbvnbtaYIH8QOE3SKaTBfAnwvsoKkpZFxI5s8ULgyez1PcBnJJX/db4duKYBbbJJkC+mlyjtHSrSO1gYPl8b4OBQscmts1bX6CF499zNUnUHeUQUJV1JGsqdwM0RsUnSdcCGiFgP/IGkC4EisBe4LNt2r6S/IP0xAHBdeeKbNV9EsPtgbji8B/Ol8TcyG0Mjh+A9ec4s1ZBj5BFxN3D3qLJrK15fQ42edkTcDNzciHZYfUpJ0DtYYP9ggb5ckSSCLbv6mt0sm2EmOgTvyXNmKV/ZbQYrT047MFjgwGCBg0PF4euPJ7XuGmI2ySY6BO/Jc2YpB/kMExEUk+BnOw/SO1ig4PO1rQVNZAje56+bpRzkM0B/rsi+gTz7+gsczKWT0vb05ZvcKrP6+Px1s5SDfBqKCHoHi+wdyLNvID/tb81pM5fPXzdzkE8rhVLC5p0H2T9Y8CVOzTIegrfpzkHexpIk2D9Y4MW+9BSxAF70kLnZCB6Ct+nOQd5mysPmu/ty7BvID/e83f82q81D8DadOcjbxMGhAi/25dnbn/P9t80mgYfgrV05yFtYvpiQKyYUSgmPb+9tdnPMpjUPwVu7cpC3oAODBXb1DrGnPz981zAzm3wegrd25CBvEcVSwu6+HDt7c76muVkL813crNU0JMglnQ98gfSmKV+JiOtHrf8T4ArSm6bsBv5TRDybrSsBj2VVn4uICxvRpnbRlyuys3eIPX1536vbrA34Lm7WajrqfQNJncCXgAuAM4BLJZ0xqtojQE9EvBq4i0P3IwcYjIg12WPGhHgxCfrzRR7bdoBdvTmHuFkbKQ/BH79wNq89eXHVwC0PwVca7y5uwcieu9lE1B3kwNnAloh4OiLywO3ARZUVIuIHETGQLf4YWNGAz21L+/rzPLbtAAP5osPbbBorD8Ery/ijuYvbaEkSPPzsPr758DYefnYfif8PMRoztL4c2FqxvA04Z4z6lwPfrVieI2kD6bD79RHx7Qa0qeUcGCiwdd8AB4eKzW6KmU0B38XNpsqUTnaT9LtAD/DrFcUnR8R2SS8F7pf0WET8vMq264B1AKtWrZqS9jbCgcEC2/YN0DvoADebaXwXN5sKjRha3w6srFhekZWNIOmtwMeACyMiVy6PiO3Z89PAA8BZ1T4kIm6MiJ6I6Fm6dGkDmj25Dg4VeOL5Xp54vtchbmY1lXvuyxfNZemCbv7gvNOq9rI9BG+1NKJH/iBwmqRTSAP8EuB9lRUknQV8GTg/InZVlC8GBiIiJ+l44I2MnAjXdkpJkCv6Ai5mNnE+f93qUXePPCKKwJXAPcCTwJ0RsUnSdZLKs9D/G7AA+AdJGyWtz8pPBzZI+inwA9Jj5E/U26ZmODhU4MkdvfTnixQT3zbUzBpropPnjmQWvHvu00NDjpFHxN3A3aPKrq14/dYa2/0b8KpGtKFZDg4V2LZvkP0DhWY3xcymMZ+/brX4ym5HyQFuZlOtkUPwnjw3fTRistuMUh5Cf3x7r0PczFqOz1+fedwjnyBPYjOzduDz12ce98jH4UlsZtZuJnIJ2cmYPGfN4R55DT4GbmbTme+/Pn04yGvYvKuPXME9cDObvnz++vTgoXUzM6vJ56+3PvfIzcysJp+/3vrcIzczszH5/uutzUFuZmZ18/nrzeOhdTMzq5vPX28e98jNzKwhmnX++kzvuTckyCWdL+kpSVskXV1l/WxJd2TrfyJpdcW6a7LypyS9oxHtMTOz1tTo+6+Xe+5fvH8zdz20jS/ev5nPfPfJGRXmdQe5pE7gS8AFwBnApZLOGFXtcmBfRJwK3AB8Ntv2DNL7l58JnA/8j+z9zMxsmvLkucZSRH2/WiS9AfhERLwjW74GICL+sqLOPVmdH0nqAl4AlgJXV9atrDfWZy45+fR420dvrqvdZU/sSK+dfsayY0aU9+WKJBV/Ns/uGQDg5OPmjfl+rud606leO7TR9aZnvYjgub2DDORLAEgwd1Ynq5bMRToU/LsP5nixL3/Y9ksXdHP8wtmHvWdfrsRQocScWZ0smN054r0aRaQ/VBrpzg/+ykMR0VP18xoQ5O8Fzo+IK7Ll9wPnRMSVFXUez+psy5Z/DpwDfAL4cUR8PSu/CfhuRNxV5XPWAesAFix72S+/8+Nfq6vd4xkd5GZmNrXKwZsrlJhdI3gPDhXZvn+Qyv+uJVi+aO6IMJ3oD4Oyen6QTHWQt82s9Yi4EbgRoKenJ+74/TdM6uc9/Nw+X6LVzKzFTXR2+8PP7uOL928eXo5I72r5G69eXvWa8Vd981GGCiXe9aqTxrxm/HX/tAmAa9915nBZV6d43eoljdxN7vxg7XWNCPLtwMqK5RVZWbU627Kh9WOBPRPc1szMrKry5LnxbtZypFeeK/fyv3j/5pY/7a0Rs9YfBE6TdIqkbtLJa+tH1VkPrM1evxe4P9Ix/fXAJdms9lOA04B/b0CbzMxshujoEK89eTHvee2Khk2eKw/Vj3fa28GhIrsP5pp62lvdQR4RReBK4B7gSeDOiNgk6TpJF2bVbgKOk7QF+BMOTXLbBNwJPAH8C/ChiCjV2yYzM7NK5fPXZ3d1IOq/8lxlz/3FvnxTT3tryDHyiLgbuHtU2bUVr4eA36qx7aeBTzeiHWZmZtVMdAh+oleeG6vnfvZLG3t8fDy+spuZmc0IExmCb3TPfSq0zax1MzOzydbonvtUcJCbmZlVKPfcR5+WVqnccx992tvonvtUcJCbmZkdoYn23KeCg7yGBbO7yBUOv+yfmZkZTKznPiXtaOqnt7CXn7iQM5cfw7FzZzW7KWZmZjW5Rz6GY+bM4oyTZtE7VGDb3kEODBaa3SQzM7MRHOQT4EA3M7NW5SA/Ag50MzNrNQ7yo+BANzOzVuEgr4MD3czMmq2uWeuSlki6V9Lm7PmwOfiS1kj6kaRNkh6V9NsV626R9AtJG7PHmnra0yxpoB/jWe5mZjbl6j397Grgvog4DbgvWx5tAPhARJwJnA98XtKiivV/GhFrssfGOtvTVA50MzObavUG+UXArdnrW4F3j64QET+LiM3Z6+eBXcDSOj+3pZUD/YyTjmHhHB+9MDOzyVNvkJ8YETuy1y8AJ45VWdLZQDfw84riT2dD7jdIml1ne1rKsXNn8UvLj+X0ZQsd6GZmNinGTRdJ3wdeUmXVxyoXIiIk1byjuqRlwNeAtRFRvl3MNaQ/ALqBG4GrgOtqbL8OWAewatWq8ZrdUhbN62bRvG729efZtm+Qvlyx2U0yM7NpYtwgj4i31lonaaekZRGxIwvqXTXqHQP8M/CxiPhxxXuXe/M5SV8FPjJGO24kDXt6enpq/mBoZYvnd7N4fjd7+/Ns3TvAQL7U7CaZmVmbq3dofT2wNnu9FvjO6AqSuoFvAX8XEXeNWrcsexbp8fXH62xPW1gyv5vXrFzEK16ykEXzPCnOzMyOXr0Hbq8H7pR0OfAscDGApB7ggxFxRVb2a8Bxki7Ltrssm6H+DUlLAQEbgQ/W2Z62smR+N0vmdzNUKLGzd4jdB3MUSm052GBmZk2iiPYLjp6entiwYUOzm9FwSRLs6c+zs3eIg0M+jm5m1o66OsXrVi9p6HtKeigieqp+XkM/yerS0SGWLpzN0oWz6c8V2dk7xIt9eUpJ+/3YMjOzqeEgb1HzZ3fx0qULOPm4YE9fjt19OQ4OFWnDARQzM5tEDvIW19khTjhmDiccM4dcscTe/jx7+vIeejczM8BB3lZmd3Wy7Ni5LDt2LkOFEi/25djTl/dpbGZmM5iDvE3NmdXJisXzWLF4Hv25Inv68uzpzzFUSMbf2MzMpg0H+TQwf3YX82d3seq4eQzki+ztz7N/oODhdzOzGcBBPs3M6+5iXncXKxZDvpiwfyDP3oE8BwYKePK7mdn04yCfxrq7OoYnypWS4MBggb39eQ4M5skXnepmZtOBg3yG6OzQ8JXkAPpzRQ4MFrIhePfWzczalYN8hiofVz9p0VySJDg4VGT/YJ4DgwX6c54Fb2bWLhzkRkeHOHbeLI7NbuCSLyYcGCxwYLBA71CBnGfCm5m1LAe5Haa7q2P4UrEAuWKJ3sEivUPpTPhBn7duZtYy6gpySUuAO4DVwDPAxRGxr0q9EvBYtvhcRFyYlZ8C3A4cBzwEvD8i8vW0yRpvdlcnSxd2Dgd7vpgMh3rvYMEXpDEza6J670d+NXBfRJwG3JctVzMYEWuyx4UV5Z8FboiIU4F9wOV1tsemQHdXB8cvmM0px8/nNSsX8brVizl92UJWLJ7LsXNn0dWpZjfRzGzGqHdo/SLg3Oz1rcADwFUT2VCSgPOA91Vs/wngb+tsk02xrs4OFs3rZtG8dEZ8RDBYKNE3VORgrkjfUNG9djOzSVJvkJ8YETuy1y8AJ9aoN0fSBqAIXB8R3yYdTt8fEeXLj20Dltf6IEnrgHUAq1atqrPZNpkkDV+Y5oSsrFhK6M+V6MsX6c8V6csVPYnOzKwBxg1ySd8HXlJl1ccqFyIiJNU6G/nkiNgu6aXA/ZIeAw4cSUMj4kbgRoCenh6f9dxmujo7OHZex/DMeIBCKaE/V6Q/X3K4m5kdpXGDPCLeWmudpJ2SlkXEDknLgF013mN79vy0pAeAs4B/BBZJ6sp65SuA7UexD9amZg0PyR8qK5QSBnIl+vNFBvJF+nMlBgsl34fdzKyGeofW1wNrgeuz5++MriBpMTAQETlJxwNvBP4q68H/AHgv6cz1qtvbzDKrSs89SdJj7v359NS3/lyJgXyRQsnpbmZWb5BfD9wp6XLgWeBiAEk9wAcj4grgdODLkhLSWfLXR8QT2fZXAbdL+hTwCHBTne2xaaijQ8NXoquULyYM5tMe+0A+nVA3VCg54M1sRqkryCNiD/CWKuUbgCuy1/8GvKrG9k8DZ9fTBpu5urs66O7q4FhmjSgfHfCDhTTgfaMYM5uOfGU3m3ZqBXyxlDCUhfxQFu5pyCeUfNcYM2tTDnKbMbo6O1jQ2cGC2Yf/tc8VSwzlk/S5kDBULIe9Q97MWpuD3Iz0MrSzuzphVC8e0pn05VAfKpTIFdPAzxUT8sXEM+rNrKkc5GbjmNXZwazODhbOOXxdRGTBnoV7IRkR9IVi4nu9m9mkcpCb1UESc2Z1MmdW9d48pJPv8qWEXKFEvpT24vNZ+JeX3as3s6PlIDebZOXJd9WOzUPaqy+Ugnwp7cEXSmnIF0pJVhbkS+lpdQ58MxvNQW7WZJLo7hLdXR0wu3a9cuAXk3K4p2GfPqLideLQN5tBHORmbWI48OmA7vHrF0sJxSSGg71YSigk2XP2g6CY/QAoJUExcfibtSMHudk01dXZQVcn2fH7iakM/1KSjgCkIZ+GfjE5tFwO/1L28I8As+ZwkJvZsKMJf0iH/cvBnkT2nIx8Lgd+KSpeZ/UPPePz9s2OkIPczOomia5O0XVk+V9TtZBPEihF+XVkr9Ob6iTZ63Rk4NDrJNKRgiTS+uV15TKPIth0UFeQS1oC3AGsBp4BLo6IfaPqvBm4oaLolcAlEfFtSbcAv86he5NfFhEb62mTmbW/zg7R2aFJ/5xysCcxMvTTsE+fqVxP+jotg+BQ3ch+IASH3icg+7GQrefQD4jKulBlOWtf5XscqkfFdkFw6D1s5qm3R341cF9EXC/p6mz5qsoKEfEDYA0MB/8W4HsVVf40Iu6qsx1mZkdMEp2CTib/R8NUihj5g6BcVrmclpXrxGFlh7/nqGUOr1hz2zHaOR1JU/v3qd4gvwg4N3t9K/AAo4J8lPcC342IgTo/18zMapDE4VkyvX6s2CEddW5/YkTsyF6/AJw4Tv1LgNtGlX1a0qOSbpBU8yxaSeskbZC0Yffu3XU02czMbPoYN8glfV/S41UeF1XWi3SMpOY4iaRlpPclv6ei+BrSY+avA5YwRm8+Im6MiJ6I6Fm6dOl4zTYzM5sRxh1aj4i31lonaaekZRGxIwvqXWO81cXAtyKiUPHe5d58TtJXgY9MsN1mZmZG/UPr64G12eu1wHfGqHspo4bVs/BH6cyAdwOP19keMzOzGaXeIL8eeJukzcBbs2Uk9Uj6SrmSpNXASuD/jNr+G5IeAx4Djgc+VWd7zMzMZpS6Zq1HxB7gLVXKNwBXVCw/AyyvUu+8ej7fzMxspqu3R25mZmZN5CA3MzNrYw5yMzOzNuYgNzMza2MOcjMzszbmIDczM2tjDnIzM7M25iA3MzNrYw5yMzOzNuYgNzMza2MOcjMzszZWV5BL+i1JmyQlknrGqHe+pKckbZF0dUX5KZJ+kpXfIam7nvaYmZnNNPX2yB8H3gP8sFYFSZ3Al4ALgDOASyWdka3+LHBDRJwK7AMur7M9ZmZmM0pdQR4RT0bEU+NUOxvYEhFPR0QeuB24KLsH+XnAXVm9W0nvSW5mZmYTNBXHyJcDWyuWt2VlxwH7I6I4qtzMzMwmaNz7kUv6PvCSKqs+FhHfaXyTarZjHbAuW+yTNN5IwJE4Hnixge/XLN6P1jJd9gOmz754P1qL92PiTq61Ytwgj4i31vnh24GVFcsrsrI9wCJJXVmvvFxeqx03AjfW2ZaqJG2IiJqT9dqF96O1TJf9gOmzL96P1uL9aIypGFp/EDgtm6HeDVwCrI+IAH4AvDertxaYsh6+mZnZdFDv6We/KWkb8AbgnyXdk5WfJOlugKy3fSVwD/AkcGdEbMre4irgTyRtIT1mflM97TEzM5tpxh1aH0tEfAv4VpXy54F3VizfDdxdpd7TpLPam21ShuybwPvRWqbLfsD02RfvR2vxfjSA0hFuMzMza0e+RKuZmVkbm/FBXuvyse1G0jOSHpO0UdKGZrdnoiTdLGmXpMcrypZIulfS5ux5cTPbOBE19uMTkrZn38lGSe8c6z1agaSVkn4g6Yns8st/mJW31Xcyxn601XciaY6kf5f002w/PpmVt9XlrcfYj1sk/aLi+1jT5KZOiKROSY9I+qdsuanfx4wO8nEuH9uO3hwRa9rsdI5bgPNHlV0N3BcRpwH3Zcut7hYO3w9IL0G8JnscNk+kBRWB/xIRZwCvBz6U/Ztot++k1n5Ae30nOeC8iHgNsAY4X9Lrab/LW9faD4A/rfg+NjargUfoD0knb5c19fuY0UFOjcvHNrlNM0pE/BDYO6r4ItJL9kKbXLq3xn60nYjYEREPZ68Pkv5ntZw2+07G2I+2Eqm+bHFW9gja7PLWY+xH25G0AvgPwFey5aZfbnymB3mty8e2owC+J+mh7Cp47ezEiNiRvX4BOLGZjanTlZIezYbeW3o4ejRJq4GzgJ/Qxt/JqP2ANvtOsmHcjcAu4F7g57Th5a1H70dElL+PT2ffxw2SZjevhRP2eeDPgCRbbvrlxmd6kE8nb4qI15IeJviQpF9rdoMaIbtwUFv+cgf+FngZ6VDiDuBzTW3NEZC0APhH4I8iordyXTt9J1X2o+2+k4goRcQa0qtfng28srktOjqj90PSLwHXkO7P64AlpNcWaVmS3gXsioiHmt2WSjM9yGtdPrbtRMT27HkX6bn9rXB+/tHaKWkZQPa8q8ntOSoRsTP7zysB/hdt8p1ImkUaft+IiG9mxW33nVTbj3b9TgAiYj/p1TDfQHZ562xVW/2/VbEf52eHQCIicsBXaf3v443AhZKeIT0Uex7wBZr8fcz0IK96+dgmt+mISZovaWH5NfB20nvFt6v1pJfshTa+dG85+DK/SRt8J9nxvpuAJyPirytWtdV3Ums/2u07kbRU0qLs9VzgbaTH+9vq8tY19uP/Vfw4FOlx5Zb+PiLimohYERGrSfPi/oj4HZr8fcz4C8Jkp598HugEbo6ITze3RUdO0ks5dIW9LuDv22U/JN0GnEt696CdwMeBbwN3AquAZ4GLI6KlJ5LV2I9zSYdwA3gG+P2K48wtSdKbgH8FHuPQMcCPkh5fbpvvZIz9uJQ2+k4kvZp08lQnacfrzoi4Lvs3fzvpcPQjwO9mvdqWNMZ+3A8sBQRsBD5YMSmupUk6F/hIRLyr2d/HjA9yMzOzdjbTh9bNzMzamoPczMysjTnIzczM2piD3MzMrI05yM3MzNqYg9zMzKyNOcjNzMzamIPczMysjf1/BsptrGgYgr0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the ACF\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "fig, ax = plt.subplots(figsize=(8,3))\n",
    "plot_acf(data_diff,ax=ax, lags= 40);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf868f02",
   "metadata": {},
   "source": [
    "From the above lags between 1 and 17 are in the statistically significant region meaning time periods within that span can affect present values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d8979fa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the PACF\n",
    "\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "fig, ax = plt.subplots(figsize=(8,3))\n",
    "plot_pacf(data_diff,ax=ax, lags=40);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b52b354",
   "metadata": {},
   "source": [
    "From the PACF, lags with high correlation and that are within the statistically significant region, are between lags 1 and 4. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90df652a",
   "metadata": {},
   "source": [
    "## 5.2 Time Series Modelling\n",
    "\n",
    "The AIC(Akaike's Information Criterion) and BIC(Bayesian Information Criterion) will be determinants of the the model perfomances. \n",
    "\n",
    "AIC is a metric that helps in the evaluation of a model that takes the results of the maximum likelihood as well as the total number of parameters. The lower the AIC the better the model performance. \n",
    "\n",
    "The BIC is similar to AIC but differs in that it considers the number of rows in the dataset. The lower the BIC the better the model performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090045e0",
   "metadata": {},
   "source": [
    "#### Baseline Model (MA Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c6181a",
   "metadata": {},
   "source": [
    "We will construct an MA model as our baseline model then increase its complexity with more complex models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed2f8cca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                  value   No. Observations:                  241\n",
      "Model:                 ARIMA(0, 0, 1)   Log Likelihood               -2620.418\n",
      "Date:                Wed, 30 Nov 2022   AIC                           5246.837\n",
      "Time:                        11:44:56   BIC                           5257.291\n",
      "Sample:                    04-01-1998   HQIC                          5251.049\n",
      "                         - 04-01-2018                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       1.484e+04   2058.583      7.210      0.000    1.08e+04    1.89e+04\n",
      "ma.L1          1.0000      0.132      7.562      0.000       0.741       1.259\n",
      "sigma2      1.652e+08    8.9e-07   1.86e+14      0.000    1.65e+08    1.65e+08\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                 241.36   Jarque-Bera (JB):                22.53\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.54   Skew:                            -0.71\n",
      "Prob(H) (two-sided):                  0.06   Kurtosis:                         2.53\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 3.41e+34. Standard errors may be unstable.\n"
     ]
    }
   ],
   "source": [
    "# Fit an ARMA model\n",
    "mod_ma = ARIMA(data_diff, order=(0,0,1))\n",
    "res_ma = mod_ma.fit()\n",
    "\n",
    "# Print out summary information on the fit\n",
    "print(res_ma.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08a0018",
   "metadata": {},
   "source": [
    "The huge nature of the cofficients are evident that our model is very unsafisfactory.This model has a high AIC of 5246.837 that justfies its unsatisfactory nature. Also, the BIC is large 5257.291 that affirms this conclusion."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "658d6f0b",
   "metadata": {},
   "source": [
    "#### AR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d012a48d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                  value   No. Observations:                  241\n",
      "Model:                 ARIMA(1, 0, 0)   Log Likelihood               -2073.427\n",
      "Date:                Wed, 30 Nov 2022   AIC                           4152.854\n",
      "Time:                        11:44:56   BIC                           4163.308\n",
      "Sample:                    04-01-1998   HQIC                          4157.065\n",
      "                         - 04-01-2018                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       1.484e+04   3.22e+04      0.461      0.645   -4.82e+04    7.79e+04\n",
      "ar.L1          0.9977      0.003    368.889      0.000       0.992       1.003\n",
      "sigma2      1.704e+06   4073.497    418.312      0.000     1.7e+06    1.71e+06\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                 220.46   Jarque-Bera (JB):                60.64\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               2.83   Skew:                            -1.13\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.98\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
      "[2] Covariance matrix is singular or near-singular, with condition number 6.61e+31. Standard errors may be unstable.\n"
     ]
    }
   ],
   "source": [
    "# Fit an AR model\n",
    "mod_ar = ARIMA(data_diff, order=(1,0,0))\n",
    "res_ar = mod_ar.fit()\n",
    "\n",
    "# Print out summary information on the fit\n",
    "print(res_ar.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b938d243",
   "metadata": {},
   "source": [
    "This second model is an improvement of the first model. It has lower AIC and BIC values that stands at 4152.854, and 4163.308 respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b88883a",
   "metadata": {},
   "source": [
    "#### ARMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3f940814",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                  value   No. Observations:                  241\n",
      "Model:                 ARIMA(1, 0, 1)   Log Likelihood               -1966.920\n",
      "Date:                Wed, 30 Nov 2022   AIC                           3941.840\n",
      "Time:                        11:44:56   BIC                           3955.780\n",
      "Sample:                    04-01-1998   HQIC                          3947.456\n",
      "                         - 04-01-2018                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       1.484e+04    3.4e+04      0.436      0.663   -5.18e+04    8.15e+04\n",
      "ar.L1          0.9973      0.003    324.030      0.000       0.991       1.003\n",
      "ma.L1          0.7497      0.038     19.600      0.000       0.675       0.825\n",
      "sigma2      6.976e+05   5.78e+04     12.064      0.000    5.84e+05    8.11e+05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                  82.73   Jarque-Bera (JB):                24.82\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               2.63   Skew:                            -0.64\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.91\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Fit an ARMA model\n",
    "mod_arma = ARIMA(data_diff, order=(1,0,1))\n",
    "res_arma = mod_arma.fit()\n",
    "\n",
    "# Print out summary information on the fit\n",
    "print(res_arma.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fff1aed",
   "metadata": {},
   "source": [
    "The third model is an improvement of the first and second model. It has lower AIC and BIC values that stands at 3941.840, and 3955.780 respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a370f6d",
   "metadata": {},
   "source": [
    "#### ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dc27ca8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                  value   No. Observations:                  241\n",
      "Model:                 ARIMA(1, 1, 1)   Log Likelihood               -1786.068\n",
      "Date:                Wed, 30 Nov 2022   AIC                           3578.136\n",
      "Time:                        12:47:14   BIC                           3588.578\n",
      "Sample:                    04-01-1998   HQIC                          3582.343\n",
      "                         - 04-01-2018                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.9388      0.010     98.014      0.000       0.920       0.958\n",
      "ma.L1         -0.2384      0.042     -5.641      0.000      -0.321      -0.156\n",
      "sigma2      1.487e+05   4248.324     34.996      0.000     1.4e+05    1.57e+05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   1.16   Jarque-Bera (JB):              2233.04\n",
      "Prob(Q):                              0.28   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.58   Skew:                             0.01\n",
      "Prob(H) (two-sided):                  0.04   Kurtosis:                        17.94\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Fit an ARIMA model\n",
    "\n",
    "mod_arima = ARIMA(data_diff, order=(1,1,1))\n",
    "res_arima = mod_arima.fit()\n",
    "\n",
    "# Print out summary information on the fit\n",
    "print(res_arima.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aeca545",
   "metadata": {},
   "source": [
    "Fantastic! Our AIC and BIC keep getting lower which means our model is performing better."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf119a5",
   "metadata": {},
   "source": [
    "#### SARIMA Model\n",
    "\n",
    "SARIMA is the ideal model to apply since it takes seasonality into account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1cd0511e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                    <td>y</td>                  <th>  No. Observations:  </th>    <td>265</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(3, 0, 0)x(0, 0, [1, 2], 12)</td> <th>  Log Likelihood     </th> <td>-1849.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                     <td>Wed, 30 Nov 2022</td>           <th>  AIC                </th> <td>3712.630</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                         <td>11:47:43</td>               <th>  BIC                </th> <td>3737.688</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                      <td>04-01-1996</td>              <th>  HQIC               </th> <td>3722.698</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td>- 04-01-2018</td>             <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                 <td>opg</td>                 <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>  151.9911</td> <td>  127.172</td> <td>    1.195</td> <td> 0.232</td> <td>  -97.261</td> <td>  401.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    1.7470</td> <td>    0.026</td> <td>   68.337</td> <td> 0.000</td> <td>    1.697</td> <td>    1.797</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>     <td>   -0.5069</td> <td>    0.053</td> <td>   -9.582</td> <td> 0.000</td> <td>   -0.611</td> <td>   -0.403</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>     <td>   -0.2407</td> <td>    0.028</td> <td>   -8.527</td> <td> 0.000</td> <td>   -0.296</td> <td>   -0.185</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th>  <td>    0.0648</td> <td>    0.050</td> <td>    1.285</td> <td> 0.199</td> <td>   -0.034</td> <td>    0.164</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th>  <td>   -0.0927</td> <td>    0.059</td> <td>   -1.561</td> <td> 0.118</td> <td>   -0.209</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 6.348e+04</td> <td>    3.004</td> <td> 2.11e+04</td> <td> 0.000</td> <td> 6.35e+04</td> <td> 6.35e+04</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>13270.97</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.99</td> <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>4.20</td> <th>  Skew:              </th>   <td>-2.34</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>   <td>37.35</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 1.53e+19. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                        SARIMAX Results                                        \n",
       "===============================================================================================\n",
       "Dep. Variable:                                       y   No. Observations:                  265\n",
       "Model:             SARIMAX(3, 0, 0)x(0, 0, [1, 2], 12)   Log Likelihood               -1849.315\n",
       "Date:                                 Wed, 30 Nov 2022   AIC                           3712.630\n",
       "Time:                                         11:47:43   BIC                           3737.688\n",
       "Sample:                                     04-01-1996   HQIC                          3722.698\n",
       "                                          - 04-01-2018                                         \n",
       "Covariance Type:                                   opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept    151.9911    127.172      1.195      0.232     -97.261     401.243\n",
       "ar.L1          1.7470      0.026     68.337      0.000       1.697       1.797\n",
       "ar.L2         -0.5069      0.053     -9.582      0.000      -0.611      -0.403\n",
       "ar.L3         -0.2407      0.028     -8.527      0.000      -0.296      -0.185\n",
       "ma.S.L12       0.0648      0.050      1.285      0.199      -0.034       0.164\n",
       "ma.S.L24      -0.0927      0.059     -1.561      0.118      -0.209       0.024\n",
       "sigma2      6.348e+04      3.004   2.11e+04      0.000    6.35e+04    6.35e+04\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             13270.97\n",
       "Prob(Q):                              0.99   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               4.20   Skew:                            -2.34\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        37.35\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "[2] Covariance matrix is singular or near-singular, with condition number 1.53e+19. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using auto_arima- it does a random search for the best pdq,PDQS  \n",
    "sarima_model = pm.auto_arima(monthly_data, \n",
    "                             m=12,\n",
    "                             seasonal=True,\n",
    "                             start_p=0,\n",
    "                             start_q=0,\n",
    "                             start_P=0,\n",
    "                             start_Q=0,\n",
    "                             max_order=6, \n",
    "                             test='adf',\n",
    "                             error_action='warn',  \n",
    "                             suppress_warnings=True,\n",
    "                              stepwise=True,\n",
    "                              trace=False)\n",
    "sarima_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4700e0f",
   "metadata": {},
   "source": [
    "The SARIMA performed worse compared to the last model. This was unexpected hence we will hyperparameter tune the model to see whether we get better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b957ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the p, d and q parameters to take any value between 0 and 2\n",
    "\n",
    "p = d = q = range(0,2)\n",
    "\n",
    "# Generate all different combinations of p, d , q and s\n",
    "pdq = list(itertools.product(p, d, q))\n",
    "pdqs = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b31e170e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ans = []\n",
    "for comb in pdq:    \n",
    "    for combs in pdqs:\n",
    "        try:\n",
    "            model = sm.tsa.statespace.SARIMAX(data_diff,\n",
    "                                            order=comb,\n",
    "                                            seasonal_order=combs,\n",
    "                                            enforce_stationarity=False,\n",
    "                                            enforce_invertibility=False)\n",
    "            output = model.fit()\n",
    "            ans.append([comb, combs, output.aic])\n",
    "           \n",
    "        except:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "20bb0828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>pdq</th>\n",
       "      <th>pdqs</th>\n",
       "      <th>aic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(0, 0, 0)</td>\n",
       "      <td>(0, 0, 0, 12)</td>\n",
       "      <td>5619.771886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(0, 0, 0)</td>\n",
       "      <td>(0, 0, 1, 12)</td>\n",
       "      <td>5197.316680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(0, 0, 0)</td>\n",
       "      <td>(0, 1, 0, 12)</td>\n",
       "      <td>5031.069847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(0, 0, 0)</td>\n",
       "      <td>(0, 1, 1, 12)</td>\n",
       "      <td>4664.560674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(0, 0, 0)</td>\n",
       "      <td>(1, 0, 0, 12)</td>\n",
       "      <td>5044.617496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>(1, 1, 1)</td>\n",
       "      <td>(0, 1, 1, 12)</td>\n",
       "      <td>3221.780753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>(1, 1, 1)</td>\n",
       "      <td>(1, 0, 0, 12)</td>\n",
       "      <td>3373.468042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>(1, 1, 1)</td>\n",
       "      <td>(1, 0, 1, 12)</td>\n",
       "      <td>3313.355113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>(1, 1, 1)</td>\n",
       "      <td>(1, 1, 0, 12)</td>\n",
       "      <td>3330.711175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>(1, 1, 1)</td>\n",
       "      <td>(1, 1, 1, 12)</td>\n",
       "      <td>3221.816273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          pdq           pdqs          aic\n",
       "0   (0, 0, 0)  (0, 0, 0, 12)  5619.771886\n",
       "1   (0, 0, 0)  (0, 0, 1, 12)  5197.316680\n",
       "2   (0, 0, 0)  (0, 1, 0, 12)  5031.069847\n",
       "3   (0, 0, 0)  (0, 1, 1, 12)  4664.560674\n",
       "4   (0, 0, 0)  (1, 0, 0, 12)  5044.617496\n",
       "..        ...            ...          ...\n",
       "59  (1, 1, 1)  (0, 1, 1, 12)  3221.780753\n",
       "60  (1, 1, 1)  (1, 0, 0, 12)  3373.468042\n",
       "61  (1, 1, 1)  (1, 0, 1, 12)  3313.355113\n",
       "62  (1, 1, 1)  (1, 1, 0, 12)  3330.711175\n",
       "63  (1, 1, 1)  (1, 1, 1, 12)  3221.816273\n",
       "\n",
       "[64 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_df = pd.DataFrame(ans, columns=['pdq','pdqs', 'aic'])\n",
    "param_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fc8bf7bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pdq         (1, 1, 1)\n",
       "pdqs    (0, 1, 1, 12)\n",
       "aic       3221.780753\n",
       "Name: 59, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_df.loc[param_df['aic'].idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f1add50f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     SARIMAX Results                                      \n",
      "==========================================================================================\n",
      "Dep. Variable:                              value   No. Observations:                  241\n",
      "Model:             SARIMAX(3, 1, 3)x(3, 1, 3, 12)   Log Likelihood               -1406.103\n",
      "Date:                            Wed, 30 Nov 2022   AIC                           2838.206\n",
      "Time:                                    11:48:41   BIC                           2880.279\n",
      "Sample:                                04-01-1998   HQIC                          2855.252\n",
      "                                     - 04-01-2018                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          1.2477      1.224      1.019      0.308      -1.151       3.647\n",
      "ar.L2          0.4152      2.308      0.180      0.857      -4.108       4.938\n",
      "ar.L3         -0.6677      1.106     -0.604      0.546      -2.835       1.499\n",
      "ma.L1         -0.5205      1.254     -0.415      0.678      -2.978       1.937\n",
      "ma.L2         -0.5125      1.486     -0.345      0.730      -3.425       2.400\n",
      "ma.L3          0.1414      0.437      0.324      0.746      -0.714       0.997\n",
      "ar.S.L12      -0.0677      0.456     -0.148      0.882      -0.961       0.825\n",
      "ar.S.L24      -0.2778      0.250     -1.109      0.267      -0.769       0.213\n",
      "ar.S.L36      -0.1100      0.277     -0.397      0.692      -0.653       0.433\n",
      "ma.S.L12      -0.6093      0.477     -1.277      0.202      -1.545       0.326\n",
      "ma.S.L24      -0.7269      0.329     -2.207      0.027      -1.373      -0.081\n",
      "ma.S.L36       0.5115      0.475      1.076      0.282      -0.420       1.443\n",
      "sigma2      3.053e+05   4.89e+04      6.243      0.000    2.09e+05    4.01e+05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):              1279.95\n",
      "Prob(Q):                              0.86   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.59   Skew:                            -1.33\n",
      "Prob(H) (two-sided):                  0.04   Kurtosis:                        15.50\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "sarima_mod2 = sm.tsa.statespace.SARIMAX(data_diff,\n",
    "                                order=(3, 1, 3),\n",
    "                                seasonal_order=(3, 1, 3, 12),\n",
    "                                enforce_stationarity=False,\n",
    "                                enforce_invertibility=False)\n",
    "\n",
    "sar_results = sarima_mod2 .fit()\n",
    "\n",
    "print(sar_results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ad97f342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>Columns</th>\n",
       "      <th>AIC</th>\n",
       "      <th>BIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MA</td>\n",
       "      <td>5246.837</td>\n",
       "      <td>5257.291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AR</td>\n",
       "      <td>4152.854</td>\n",
       "      <td>4163.308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARMA</td>\n",
       "      <td>3941.840</td>\n",
       "      <td>3955.780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>3578.136</td>\n",
       "      <td>3588.578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SARIMA</td>\n",
       "      <td>3712.419</td>\n",
       "      <td>3730.317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SARIMA(tuned)</td>\n",
       "      <td>2838.206</td>\n",
       "      <td>2880.279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Columns       AIC       BIC\n",
       "0             MA  5246.837  5257.291\n",
       "1             AR  4152.854  4163.308\n",
       "2           ARMA  3941.840  3955.780\n",
       "3          ARIMA  3578.136  3588.578\n",
       "4         SARIMA  3712.419  3730.317\n",
       "5  SARIMA(tuned)  2838.206  2880.279"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparison of the models\n",
    "columns = {'Columns': ['MA','AR','ARMA', 'ARIMA', 'SARIMA', 'SARIMA(tuned)'],\n",
    "'AIC':['5246.837', '4152.854', '3941.840', '3578.136', '3712.419','2838.206'],\n",
    "'BIC':['5257.291', '4163.308', '3955.780', '3588.578', '3730.317', '2880.279']\n",
    "}\n",
    "pd.DataFrame(columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a61a49cd",
   "metadata": {},
   "source": [
    "After fitting all models, the tuned SARIMA model had the least AIC (2838) making it the best model, as seeing in the table above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14773d9",
   "metadata": {},
   "source": [
    "## 5.3 Model Evaluation and Forecasting\n",
    "The ability of a time series forecasting model to predict the future is defined by its performance. This is frequently at the expense of being able to explain why a particular prediction was made, confidence intervals, and even a greater grasp of the problem’s underlying causes. We will use the mean absolute percentage error(MAPE) to see how well our model is performing.The model is better if the MAPE is low. This section is going to compare dynamic and non-dynamic forecasts in relation to our data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b06b280f",
   "metadata": {},
   "source": [
    "#### Non-Dynamic Forecasting\n",
    "Forecasting is an essential aspect of time series modeling and its accuracy is essential when advising clients. Non-dynamic forcasting refers to creating forecasts using the lagged dependent variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f929fcf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = sar_results.get_prediction(start=pd.to_datetime('2010-01-01'), dynamic=False)\n",
    "\n",
    "pred_ci = pred.conf_int() # this gives us the confidence interval for our forecasts\n",
    "\n",
    "plt.figure(figsize = (15,8))\n",
    "ax = data_diff['2012':].plot(label='observed',color='black')\n",
    "\n",
    "pred.predicted_mean.plot(ax=ax, label='Forecast', color='maroon',alpha=0.8)\n",
    "\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=0.5)\n",
    "\n",
    "ax.set_title('Dynamic Forecast from 2010-2018')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Amount')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4ba2a0b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012825766355036086"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model evaluation\n",
    "\n",
    "y_ndynamic = pred.predicted_mean\n",
    "y_actual = data_diff['2010-01-01':]\n",
    "\n",
    "mean_absolute_percentage_error(y_actual, y_ndynamic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c4d311",
   "metadata": {},
   "source": [
    "This means that forecasted results will be off by approximately 0.012% which is quite low and implies that our model perform very well. Researchers have often proved that the dynamic forecast can be a better choice so we will also explore it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36cbca1e",
   "metadata": {},
   "source": [
    "#### Dynamic Forecasting\n",
    "The advancement in technolgy and the need for fast solutions has ehanced the application of dynamic forecasting in businesses today. Dynamic forecsating provides a faster and more accurate method of forecastig in a business setting. According to [Workday.com]('https://blog.workday.com/en-us/2021/dynamic-forecasting-a-north-star-for-businesses-in-uncertain-times.html'), dynamic forecasting relies on real-time data updated on arolling basis and it imporves the insights drawnf from such data. In this case, we use dynamic forecasting in our modeling to enhance the results we get from our data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "edea3a07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dynamic Forecasting\n",
    "\n",
    "pred_dynamic = sar_results.get_prediction(start=pd.to_datetime('2010-01-01'), dynamic=True)\n",
    "pred_dynamic_ci = pred_dynamic.conf_int()\n",
    "\n",
    "# Visualizing forecasted values\n",
    "\n",
    "plt.figure(figsize = (15,8))\n",
    "ax = data_diff['2012':].plot(label='observed',color='black')\n",
    "pred_dynamic.predicted_mean.plot(ax=ax, label='Forecast', color='red',alpha=0.8)\n",
    "\n",
    "ax.fill_between(pred_dynamic_ci.index,\n",
    "                pred_dynamic_ci.iloc[:, 0],\n",
    "                pred_dynamic_ci.iloc[:, 1], color='k', alpha=.5)\n",
    "\n",
    "ax.set_title('Dynamic Forecast from 2010-2018')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Amount')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6ffa9284",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.242486761476446"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation of the Dynamic Forecast\n",
    "y_dynamic = pred_dynamic .predicted_mean\n",
    "y_dactual = data_diff['2010-01-01':]\n",
    "mean_absolute_percentage_error(y_dynamic, y_dactual)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3fd9f9",
   "metadata": {},
   "source": [
    "The dynamic forecast has a higher mean absolute percentage error that is the values predicted for the future will be off by approximately 5%. Hence, non-dynamic forecast results is a better prediction of the future in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff5264d",
   "metadata": {},
   "source": [
    "#### Future Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f8c821b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#predicting 5 years into the future\n",
    "\n",
    "pred_fut = sar_results.get_forecast(steps= 72)\n",
    "\n",
    "# Get confidence intervals of forecasts\n",
    "pred_ci = pred_fut.conf_int()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "590f8a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Forecasting\n",
    "\n",
    "ax =data_diff.plot(label='observed', figsize=(15, 8))\n",
    "pred_fut .predicted_mean.plot(ax=ax, label='Forecast')\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=.25)\n",
    "ax.set_title('House Value in 5 Years Time')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Amount')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1fa845e",
   "metadata": {},
   "source": [
    "Our model predicts a slight increament which stagnates over the next five years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "28b1ec9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Long Term Investment\n",
    "\n",
    "#prediction 20 years into the future\n",
    "\n",
    "pred_fut_20 = sar_results.get_forecast(steps= 240)\n",
    "\n",
    "# Get confidence intervals of forecasts\n",
    "pred_ci = pred_fut_20.conf_int()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "be306ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing house values in 20 years\n",
    "ax =data_diff.plot(label='observed', figsize=(15, 8))\n",
    "pred_fut_20 .predicted_mean.plot(ax=ax, label='Forecast')\n",
    "ax.fill_between(pred_ci.index,\n",
    "                pred_ci.iloc[:, 0],\n",
    "                pred_ci.iloc[:, 1], color='k', alpha=.25)\n",
    "ax.set_title('House Value in 20 years time')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Amount')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f073400",
   "metadata": {},
   "source": [
    "Over the next 20 years the growth is still very minimal. This is in line with the Zillow homes forecast which expects that over the years there will be a considerable slow growth rate in the home value from the current 14.1% to 1.4% in the future. This is because the share of income spent on monthly mortgage payments has risen from 27.7% in February to 37.3% in October – well above a previous peak of 35% in 2006.\n",
    "Housing payments are considered to be a financial burden when they exceed 30% of a household’s income."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c9c1bb",
   "metadata": {},
   "source": [
    "# 6. Conclusion\n",
    "* There are several discrepancies within the housing investment sector. Investors would rather invest in some cities than others, which is evident from the varying number of properties in the different cities.\n",
    "* The top three cities with the highest median house prices are Atherton (California), Palm Beach(Florida), and Snowmass Village (Colorado).\n",
    "* The cities with the highest ROI in 22 years are New York, Georgia state, New York, and Hawaii.\n",
    "* The prices of houses also differ concerning the city ranks. Homes in highly-ranked cities have high median prices.\n",
    "* Other factors, such as location also dictate changes in the price of house prices. For instance, houses in the coastal regions have higher median prices.\n",
    "* Given that SARIMA is our best model, seasonality patterns are just as significant when projecting property values as past values.\n",
    "* Using our model, we can also infer that the increase in home value would inevitably slow down significantly over the next few years.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d90f31",
   "metadata": {},
   "source": [
    "# 7. Recommmendations\n",
    "We advise the Horizon Real Estate Company;\n",
    "* The company should focus on locations with great beaches and outdoor activities since houses in these locations fetch highest prices as this is seen from cities in California and Florida.\n",
    "* Invest in the cities, New York and California, which has the highest Return On Investment. \n",
    "* Sell at a discounted price to increase sales over the next few years due to the slowing down of real estate price in the foreseeable future."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "0379c5dc27c8f30c5a861518db720bc3b449ce4900b226c2d0de125fac4add75"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}