problem02.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting problems\n",
    "\n",
    "Here we can find different approaches to forecast 3 different consumer package goods products, from statistics to deep learning models.\n",
    "\n",
    "- Producto A: Natural organic health product - Peru     - Monthly\n",
    "- Producto B: Coffee-based drink             - Colombia - Monthly\n",
    "- Producto C: Carbonated beverage (soda)     - Ecuador  - Daily"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import sys\n",
    "sys.path.append('src/forecast/')\n",
    "import statistics\n",
    "\n",
    "from statistical import StatisticalModel, plot_decomposition, print_adf_test, prepare_exog\n",
    "from deeplearning import DeepLearningModel, scale_data, time_series_to_X_y\n",
    "from automl import AutoMLModel, plot_prophet_predictions\n",
    "\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_paths = [\"data/producto_A.csv\", \"data/producto_B.csv\", \"data/producto_C.csv\"]\n",
    "\n",
    "for i in df_paths:\n",
    "    df = get_data(i)\n",
    "    df.set_index(\"ds\", inplace=True)\n",
    "    max_lags = min(int(len(df) / 2) - 1, 30)\n",
    "    tsplot(df.y, lags=max_lags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Producto A\n",
    "Monthly demand forecast for natural organic health product sold through retail channels in Peru. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 2)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_data(\"data/producto_A.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Model (SARIMA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_TEST = 4\n",
    "df.set_index(\"ds\", inplace=True)\n",
    "y_train, y_test = df[\"y\"][:-N_TEST], df[\"y\"][-N_TEST:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - Autoarima approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ADF test ---\n",
      "ADF Statistic: -0.918 \n",
      "p-value: 0.782\n"
     ]
    },
    {
     "data": {
      "image/png": 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dFUVstDrgd0yUIisljv4ZSfRIig2pwRMRNu+t47ONe1m2vRyFIjk+mpT4WFLio0lJiCE5PoYUs0VFKYoqHRRW6jaawsoGCisaKK52HNCmDJCWEENueiK5PRLITU+gd1oiuekJ9EpPICs5jozkODKT4vxuuzmYJpeH0tpGahtdDMxKCksP4ubM9JsrClm2vQLQbdepCbGkJcTofWIsqQkxpCXEMrJ3Kj86emBEZHasgbL4hcvtYX1xDUu3lvPNNr01l1TiYqJIidcfxuZ9stlX1jvZsKfmAKPSMzWeEb1SGdE7lWE5KdQ4XGwprWNraS1bS+vYU31grjc7JY7eXh+b3ukJXvtEeqclBPyDE4k0G6uP15dQWe+k3qkzA44mN/VOXZ3cmbGAqfEx9MtMon9Goq52ykyif2YiAzK1kQ9EL9GqeidfbC7ls417WbyplMLKBgD6ZyYSFx1Fncnc1Da23lkoOkrROy2BvhmJ9OuRSN+MRPqafZ8e+j1IDuFaRZFMjcNpSoGHR7qwBsoSUESEmkYXibHR7X7ARIS9NY2sL65hQ3ENG/bo/cY9Nfuq2jKTdbVX8zY4O9l0IEnuFsYnULg9sm9ogtsjuDyC2yM43Z4Dfje5PZRUN7KrQnf42FnRwI7yenZV1O9rZwXdS3RoTgojc1MZ1TuNkbm6FNwz9dAZBhxOt2nbcVLZ0ERlvZONe2r4bONe8ndW4hFtDKcPzWLm8J7MHNaT/pkHDgb1eIR6p5tah2ufwXJ7PPROT6RXajwxdkhFl8QaKEvE4fYIhRUNpCXGHHYdMboqIsLe2kZ2ljewvayODXtqWF9Uw/ri6gNKtlnJcQzMSqK+yU1lve500FK7nlIwvl8PjhuWzczhPZnYv4c1MpZDaM1A2fKwJWxERykGZIV/OhXLfpRS5KQmkJOawJSBGQecK69rYn1xNRuKtdHaXl5HZnI84/rGkmF6iWYkxZGRFEt6kj7uk55IepLvvdQsFm+sgbJYLD6RmRzH9CHZTB8S+pkGLN2TLlHFp5SqATaEWweQDZSGW4QhUrRYHQcSKTogcrRYHYcSKVpCpWOgiByyLEVXKUFtaKn+MtQopZZFgg6IHC1WR2TqgMjRYnUcSqRoCbcO21ppsVgslojEGiiLxWKxRCRdxUA9Hm4BhkjRAZGjxeo4kEjRAZGjxeo4lEjRElYdXaKThMVisVi6Hl2lBGWxWCyWLoY1UBaLxWKJTEQk5BvQH/gEWAusAa437pnAAmCT2WcY95HAEqARuMnLnxFAvtdWDdzQSpiz0GOlCoBbvXRsBJoAAW4Phw7j/jLQADjM/v4wPpMVXlqWoocjdEiHOXejid/VwAtAQis6LjP+bgIu89KxDXAC7s6+IwHSUWH8bQB2ATmh1mHcrwFqTbyU+vlMrjc61rT2fgQ73QRIR5HR0Rw3E0Otw7hfCNSYuKkEbmznmVwCrARWAV8CE9oLI0jpJig6jPun5v58s+W05kerfnf0hkBsQC4w2Rynmpd9NHC/18t3K3CfOc4BjgDuOfjF8vIzGihGD/hq6dxmYDAQB3xnwssFfgDkAdtNRIRchzn3EnBzBDyTPuae4UZHGXBHR3UAfYGtQKL5/TJweQs6MoEtZp9hjjNM3Fxm9rWdfR4B0rEMmOpPvARARxb6A3yiue45YGcntYxFf4yT0JmPhcDQUKebAOl4Gzjfz7jxV0cUUAicY667F52G2tIynf1G4nTg6/a+EUFKN0HRYc59Ckz1x1aEpYpPRIpE5FtzXAOsQyfgOcDT5rKngXPMNSUi8g06V9AaJwGbRWR7C+eOBApEZIuINAEvAnOMjhdEZBs6J7gpHDrMuQb0Byysz8T4VyciG42OtcBZndQRAyQqpWLQiX93C9ecBiwQkXIRqUDn7maZuHlaRIrMdf48D790oBN6IOKl0zrQH4oNIvKxuW4e4OqkllHoD1G9iLiARcC5LWgJdrrxWwdQbsLwJ2780oHOPDhE5E1z3XuAakfLlyZ+Ab4C+rUTxsEEKt0ERUcL13WKsLdBKaXygEnA10AvrwdbDPTqgFcXoatNWqIvOrfZzC7j5k0MMC7MOu5RSq1USj1J+J5JKRCjlJpq4mYCkNJRHSJSCDwA7EBXw1SJyPwO6PBG0cnnEUAd/1FKrQWOC5OOAmCEUirPGLiLTZideUdWAzOUUllKqSRgNrpqyFct3viTbgKl4x6l1DrgBHT1dKh17Eszxv0KdGnC12dyJfCBj/+1PS3edDTdBEPHf5RS+UqpO1UnVkYMq4FSSqUAr6HrfKu9z4kuI4qP/sQBZwOv+KGjJ/DbMOq4DV1Hfjw6d/NFOLSYMC4CHkbXx3/IQTlOX3QopTLQOa5B6GrDZKXUDzuixfiTAiTQyXckQDouAY5Gt11sw+Q+Q6nD5E5/jq4K/gKYDKzqzDMRkXXAfcB8dEksH91e0SH8TTcB0nEbMAVdA7Ec3U4XUh1eaeYhpdQydFVZoS/PRCl1Atow3OJreL7Q0XQTJB2XiMg4YIbZLu2oB2EzUEqpWLRxek5EXjfOe5RSueZ8LlDio3enA9+KyB5zb39jtfOVUlej64e9c0T9jJu3jjp00TwsOkzuJgZd4nkRXaceFi3oNpda4E7gCXQ9dkd1nAxsFZG9IuIEXgemK6Wmeek4uy0dXnHj8uMd8VuHCeM14FlgLrr6I+Q6ROQd4Fh0I/wis3XmmSAiT4rIFBGZie4EsjEc6cZfHejSy2vA/4C76Vzc+K1DRJYAJ6LbbN8CvmnvmSilxgP/RldXlhnnFsMIZroJlg5Ta9Bc/fo8Om46hvjRgNXZDV30fAaYe5D7XziwIe/+g87fRQsdAtAf9CvaCC8G3Xg3iP0NfmO8daBzxtnh0GHO5XppmQvcG8Zn8rLREA98hE54HdIBTEOXwJKMn08D17agIxPd9pZhtq3GzTtuajv7jgRARwy61DIXiAVeBa4OtQ5zLsc8k8fQufzhnX1HMD2qgAHAeqBHONJNAHS8YnQos7831DoOiptHMWmmrWdiwikApvsaRjDSTRB1xHi9F/vSTWvfo1a/Ux29IRAbOhco6O6N+WabjW5s/Ajd6LqQ/QmzN7pusxqde9wFpJlzyehcS3o7Yc5GlwQ2o6skvHU0d5d1oov4IdVh3JcbDQ50w+/KMD8Th9kK/dBxNzqxr0aXPuJb0fFjdCIpwBjVFuKmCd1zLdQ6TjbhN5htL3BmqHUY9wVecbMN/9LNYnQHmO+Ak8KYbgKhozluytGdG0Kqw7g/7xU3hT7Ezb/RJbV8sy1rL4wgpZtg6UhGf9NWojNmDwPRHbUVdqoji8VisUQkYe/FZ7FYLBZLS1gDZbFYLJaIxBooi8VisUQk1kBZLBaLJSKxBspisVgsEYk1UBaLxWKJSKyBslgsFktEYg2UxWKxWCISa6AsFovFEpFYA2WxWCyWiMQaKIvFYrFEJDHhFhAIsrOzJS8vL9wyLBaLJeh4BGocTtITY8MtJWAsX768VER6HuzeJQxUXl4ey5YtC7cMi8ViCSpuj/CzZ5fz0fo9vHDDTIb1Sm3/psMApdT2ltxtFZ/FYrEcBogId7+zhoXr9vD7M0d3GePUFtZAWSwWy2HAE4u38MyS7fx0xiAuP2ZQuOWEhJAZKKVUtFJqhVLqXfN7kFLqa6VUgVLqJaVUnHGPN78LzPm8UGm0WCyWSOTdlbv5v/fXc8a4XG47fVS45YSMUJagrgfWef2+D3hIRIaiV3S80rhfCVQY94fMdRaLxdItWbq1nF+99B1H5GXw1wsmEBWlwi0pZITEQCml+gFnoJcXRimlgBPR69QDPA2cY47nmN+Y8yeZ6y0Wi6VbUVBSy0+fWUa/zESe+NFUEmKjwy0ppLTbi08pdW5b50XkdR/CmQv8Bmhu1csCKkXEZX7vAvqa477ATuO3SylVZa4vPUjXVcBVAAMGDPBBgsVisRw+lNQ4uPw/S4mNVjx9xZH0SIoLt6SQ40s387PMPgeYDnxsfp8AfAm0aaCUUmcCJSKyXCl1fOdkHoqIPA48DjB16lQJlL8Wi8USbuqbXFz532WU1Tbx0s+Oon9mUrglhYV2DZSIXAGglJoPjBaRIvM7F/ivD2EcA5ytlJoNJABpwMNAD6VUjClF9QMKzfWFQH9gl1IqBkgHyjrypywWi+VwxeX28MvnV7BmdxVP/Ggq4/v1CLeksNGRNqj+zcbJsAdot25NRG4TkX4ikgdcBHwsIpcAnwDnm8suA94yx2+b35jzH4uILSFZLJYuj4jw+7fX8PH6Ev4wZywnjeoVbklhpSMzSXyklPoQeMH8vhBY6EfYtwAvKqX+BKwAnjTuTwLPKqUKgHK0UbNYLJYujYhw37wNPPf1Dq4+bgg/PGpguCWFHdWRwonpMDHD/PxMRN4IiqoOMnXqVLFTHVkslsOZv87fwKMfF3DJtAH86ZyxdKfOy0qp5SIy9WD3Ds3FZ3rs+dJrz2KxWCw+8shHm3j04wIuOqI/f5zTvYxTW/jcBqWUOlcptUkpVaWUqlZK1SilqoMpzmKxWLo6//i0gAcXbOS8yf34v++N61YDcdujIyWo+4GzRGRdu1daLBaLpV2e+GwL98/bwJyJfbj//PHWOB1ER3rx7bHGyWKxWALDU59v5Z7313HG+Fz++v0JRFvjdAgdKUEtU0q9BLwJNDY7+jiThMVisVgMzy7Zxh/eXcusMb2Ze+FEYqLtwhIt0REDlQbUA6d6uQm204TFYrH4zAtLd3DnW2s4eVQOj/xgErHWOLWKzwaqeUYJi8VisXSOF5fu4PY3VnH8iJ78/ZLJxMVY49QWHenFN1wp9ZFSarX5PV4pdUfwpHU/Pt9UyjNLtrG+uBqPx06eYbF0FSrrm7juhRXc+voqjh2azT9/OIX4mO41M3ln6EgV3xPAzcC/AERkpVLqeeBPwRDWHflgdRHPfb0DgPTEWI7Iy+CIvEyOGJTJuL7ptirAYjkM+Xj9Hm59bRXldU38+pTh/Pz4IbbNyUc6YqCSRGTpQQPIXK1dbOk4fzpnLFcfN4Svt5bzzdZyvtlWzsJ1JQAkxkYzaUAPjhyUyZSBGYzv14P0xNgwK7ZYLK1R43Dyp3fX8dKynYzsncp/rjiCMX3Swy3rsKIjBqpUKTUE3TECpdT5QFHbt1g6glKK/plJ9M9M4vwp/QC9JsyybRUs3VrO0q3lPPzRJppnpxrcM5mJ/XowcUAPJvTrwcjcVFttYLFEAF9uLuXmV1ZSVNXANccP4fqTh9m02Ql8notPKTUYvf7SdPQS7VuBS0Rke/Dk+UZ3mouv2uFk5c4qvttVSf5Ove2t0b3+46KjGN0njQn90hmak0L/zCQGZiXTt0eibYy1WEJAQ5Ob++at579fbmNwdjIPXDCByQMywi0r4vFrLj6lVDRwjYicrJRKBqJEpCbQIi3tk5YQy7HDsjl2WDagZ0AuqnLw3c5K8ndVkr+jkleW76K+yb3vnigFfXokMiAziYFZSQzITCY3PYG0xBjSEmJJS4wlLSGW1IQYkuKi7TxgFksHEBHWFdXwecFeXli6k62ldVw+PY9bZo0kMc6WmvzBlyXfY8zS68cCiEhd8GVZfEUpRZ8eifTpkcjp43IBnWD21jSyvbye7WX17CirY3t5PTvK65m/Zg9ldU2t+hcdpUhLiCEtMZZ0s2UkxdEjKZYeSXH0SIwlIzmWHolxpCXGkhgbTVJcNIlx0STERpMYG01stLJGztKlKal2sHhTKZ8XlLJ4UymltboWY2TvVJ7/6TSmD8kOs8KugS8lqKXAZGCFUupt4BVgn5GyM0lEHkopctISyElL4Ii8zEPO1ziclNQ0UuNwUd3gpNrhPOC4usFFtcNJVYOTinonO8vrqWzQv32pEY6OUiTGGoMVF0VibDSJcTEkxjYf7zdmSXHRpCXE7jOA+42h/p1sS3SWMONyeyisbGDz3lqWbC5j8aZS1hfrCqSs5DhdozE0mxnDetI7PSHMarsWHekkkYBeev1EdEcJhZ1J4rAkNSGW1ISO9wB0e4QahzZalfVNVDU4cTg9OJxuGpxu6pvc+rhp/+9Gr3MNTjdldU3UV+hrHE43dU0uHE5Pq2HGRitS4mNIjo/Zt9fH0STH6ePUBH0uxez171iv4xgS46KJi46yk3FaWqS5qnxbaR1bSuvYVlrHtjJ9vLO8Hqdb58zioqOYmpfBLbNGMmNYNqNz0+w7FUR8MVA5SqlfAavZb5iasaNJuxHRUUpX8yXFAckB87fR5aaqwUllvd4q6puoMvvKBie1Dhd1jS5qzVZV30RhhYu6Rrd2b3L5VLID/YGJj40iPiaa+Bh9nBATTVxMFDFRiqgoRbRSRO871v87yrhFR6l918VEebtFER2lSIqLJjUhhtSE/QYyNSGWtARtQNMTY0mK69AybJYAU1bbyIY9NWwsrmHDnho2FNewcU8ttY37R83Ex0QxKDuZ4TmpnDamN4OyksnLTmZc33TbrhRCfEkp0UAKBxqmZqyBsvhNfEw0OanR5KR2rnpERKhvclPb6KLGoY1YnddxrcNJg9NDo8tNo0uX+Lz3jeac2yO4PYLL46HRJbgFPMbNI3rvNnuXW7u5zHnt5qHe6W7XWKYlxNA3I4m+PRL2tR/29dr3TI23M1sHAJfbw5bSOlYXVrG6sJoNe6rZUFy7r70I9ID4Eb1TOXdyX4blpDC4Zwp52cnkpiXYklEE4IuBKhKRPwRdicXSSZRS+6r+eqWFV4vHI9Q1ufYZyxrTvte8VTY0UVzlYHdlA7sqGli6tZxqx4Hj3aMUZKXE0ystnpzUBHJS48lJjadn2v7j7JR4MpPjbK9LQ5PLw6aSGtYUVrN6dxWrCqtYV1S9r/o4ITaK4b1SOX5ET0b2TmV4r1RG9k6lZ2q8fX4RjC8GysaexeIjUVFqXxtfro+TBtQ4nBRVOSisbKCwooGSagclNY3sqXawp9rBqsIqymobaWl6xviYKLKS48hMiSMzOV4fJ8fRMzVel87SE8jtkUiv1PguMb2OiFBY2cDGPbpaTu9r2FhcS5NbG6OU+BhG90nj4iMHMrZvGuP6pjO4Z4otlR6G+GKgTvInAKVUf+AZoBe6SvBxEXlYKZUJvATkAduAC0SkQunszMPAbPTyHpeLyLf+aLBYIplmgza8V2qr17jcHsrrmiipaaSkxkFpbRPldXorq22ivK6R8romtuytpbyu6YBxcKBLZb3SEshN31+t2DstgV5pCfROj6dXWgI5qQkRMaDb4XRTUq3/Z0lNI4UVxiCV1FKwp4Y6r//WKy2e4b1SueKYPMb0TWdsnzTyspJt9VwXweeZJDodgFK5QK6IfKuUSgWWA+cAlwPlInKvUupWIENEblFKzQauRRuoacDDIjKtrTC600wSFosvNJfKdlc2sLvSQVGV3u+ubNDHVQ6aXIf2nsxOidNGKy2BnLR4EmJ1B5K4aL3FxnjvFcq7gqWFQxFwejy43ILT7cFl2uqc5neTy0NZXZM2RtW61HhwlWezruG9dNXcsF4p+jgnlfQkOx9lV8CvmST8QUSKMHP2iUiNUmod0BeYAxxvLnsa+BS4xbg/I9pyfqWU6qGUyjX+WCwWH2ivVCYiVNQ7Ka7S1YjF1Y59x3uqHeyucvDdrkrdgcQYkkATHaWIjVZkJceTkxbPkJ4pHD0kS7ez7Wtv06W+jOS4gIdviXxC2t9VKZUHTAK+Bnp5GZ1idBUgaOO10+u2XcbtAAOllLoKuApgwIABwRNtsXRBlFJkmvaq0X3a71kipsei0+3B6RIa3e59Y4Oaz+8/9g4HYqN1F/6Y6Chio3WX/Oau+hZLW4TMQCmlUoDXgBtEpNq754yIiFKqQ3WNIvI4evJalFI1SqkNgdTbSbKB0nCLMESKFqvjQCJFB0SOFqvjUCJFS6h0DGzJMSQGSikVizZOz3lNjbSnuerOtFOVGPdCoL/X7f2MW1tsaKn+MtQopZZFgg6IHC1WR2TqgMjRYnUcSqRoCbeOoHfZMb3yngTWiciDXqfeBi4zx5cBb3m5/0hpjgKqbPuTxWKxdD9CUYI6BrgUWKWUyjdutwP3Ai8rpa4EtgMXmHPvo3vwFaC7mV8RAo0Wi8ViiTBC0Yvvc1of7HvIGCvTe+8XHQzm8Y7qChKRogMiR4vVcSCRogMiR4vVcSiRoiWsOoI+DspisVgsls4Q/mHjFovFYrG0gDVQFovFYolMRCTkG7ob+SfAWmANcL1xzwQWAJvMPsO4jwSWAI3ATV7+jADyvbZq9DirlsKcBWxAd7641UvHRqAJPU/g7eHQYdxfBhoAh9nfH8ZnssJLy1J0W2WHdJhzN5r4XQ28ACS0ouMy4+8m4DIvHdsAJ+Du7DsSIB0Vxt8G9MDxnFDrMO7XALUmXkr9fCbXGx1rWns/gp1uAqSjyOhojpuJodZh3C8EakzcVAI3tvNMLgFWAquAL4EJ7YURpHQTFB3G/VNzf77Zclrzo1W/O3pDIDYgF5hsjlPNyz4auN/r5bsVuM8c5wBHAPcc/GJ5+RmNnpFiYCvnNgODgTjgOxNeLvAD9IS1201EhFyHOfcScHMEPJM+5p7hRkcZcEdHdaBn/9gKJJrfL6Mn/j1YRyawxewzzHGGiZvLzL62s88jQDqWAVP9iZcA6MhCf4BPNNc9h55xpTNaxqI/xknozMdCYGio002AdLwNnO9n3PirIwo9VvMcc9296DTUlpbp7DcSpwNft/eNCFK6CYoOc+5TYKo/tiIsVXwiUiRmhnIRqQG85+d72lz2NHpSWUSkRES+QecKWuMkYLOIbG/h3JFAgYhsEZEm4EVgjtHxgohsQ+cEN4VDhznXgP6AhfWZGP/qRGSj0bEWOKuTOmKARKVUDDrx727hmtOABSJSLiIV6NzdLBM3T8v+MXD+PA+/dKATeiDipdM60B+KDSLysbluHuDqpJZR6A9RvYi4gEXAuS1oCXa68VsHUG7C8Cdu/NKBzjw4RORNc9176J7LbWn50sQvwFfoCQnaCuNgApVugqKjhes6RdjboHycn88XLkJXm7REa/P7eRMDjAuzjnuUUiuVUk8SvmdSCsQopaaauJmAXlG5QzpEpBB4ANiBroapEpH5HdDhjaKTzyOAOv6jlFoLHBcmHQXACKVUnjFwF5swO/OOrAZmKKWylFJJ6HGH/Vu4LtjpJlA67jGTUJ+Arp4OtY59aca4X4EuTfj6TK4EPvDxv7anxZuOpptg6PiPUipfKXWn6sTKkGE1UAfPz+d9TnQZUXz0Jw44G3jFDx09gd+GUcdt6Dry49G5my/CocWEcRF6Ta41wIcclOP0RYdSKgOd4xqErjZMVkr9sCNajD8pQAKdfEcCpOMS4Gh028U2TO4zlDpM7vTn6KrgL4DJwKrOPBMRWQfcB8xHl8Ty0e0VHcLfdBMgHbcBU9A1EMvR7XQh1eGVZh5SSi1DV5UV+vJMlFInoA3DLb6G5wsdTTdB0nGJiIwDZpjt0o56EDYD1db8fOa89/x87XE68K2I7DH39jdWO18pdTVtzO/npaMOXTQPiw6Tu4lBl3heRNeph0ULus2lFrgTeAJdj91RHScDW0Vkr4g4gdeB6UqpaV46zm5Lh1fcuPx4R/zWYcJ4DXgWmIuu/gi5DhF5BzgW3Qi/yGydeSaIyJMiMkVEZqI7gWwMR7rxVwe69PIa8D/gbjoXN37rEJElwInoNtu3gG/aeyZKqfHAv9HVlWXGucUwgplugqXD1Bo0V78+j46bjiF+NGB1dkMXPZ8B5h7k/hcObMi7/6Dzd9FChwD0B/2KNsKLQTfeDWJ/g98Ybx3onHF2OHSYc7leWuYC94bxmbxsNMQDH6ETXod0oBebXINua1Hoeu9rW9CRiW57yzDbVuPmHTe1nX1HAqAjBl1qmQvEAq8CV4dahzmXY57JY+hc/vDOviOYHlXAAGA90CMc6SYAOl4xOpTZ3xtqHQfFzaOYNNPWMzHhFADTfQ0jGOkmiDpivN6Lfemmte9Rq9+pjt4QiA2dCxR098Z8s81GNzZ+hG50Xcj+hNkbXbdZjc497gLSzLlkdK4lvZ0wZ6NLApvRVRLeOpq7yzrRRfyQ6jDuy40GB7rhd2WYn4nDbIV+6LgbndhXo0sf8a3o+DE6kRRgjGoLcdOE7rkWah0nm/AbzLYXODPUOoz7Aq+42YZ/6WYxugPMd8BJYUw3gdDRHDfl6M4NIdVh3J/3iptCH+Lm3+iSWr7ZlrUXRpDSTbB0JKO/aSvRGbOHgeiO2go71ZHFYrFYIpKw9+KzWCwWi6UlrIGyWCwWS0RiDZTFYrFYIhJroCwWi8USkVgDZbFYLJaIxBooi8VisUQk1kBZLBaLJSKxBspisVgsEYk1UBaLxWKJSKyBslgsFktEEtPWSaXU5LbOi1l00GKxWCyWQNPmXHxKqU/auFdE5ES/AldqG1CDXnvFJSJTlVKZ6Nmj89ATYl4g+1d8bJHs7GzJy8vzR4rFYrFYwsTy5ctLRaTnwe5hnSzWGKipIlLq5XY/UC4i9yqlbkWvb9/mIlpTp06VZcuWBVdsGBAR6pvcVDU4qXY4qW5wUd3gZGRuKv0ykjrl596aRu56Zw0ej/CPSybTiUUuLYDHI9Q06viodjipanBS3+jmiLxM0pNiO+XnhuIa7nxrNUcPzuLGU4YHWHH3weX2UONw7UszVQ1OnB4PxwzJJi6mc60an24o4d4P1nPdScOYPS43wIq7D26PUGvipqrBuS/9nD6uz3IRmXrw9W1W8XmjlBoLjEav0giAiDwTGNkHMAe9qizoNXM+JcCrTUYq768q4l+LNlPVYCLP4cLtOTQDERcTxc9mDuaa44eSGBfts/8frCrit2+upqK+CRF4+7vdzJnY0irOloP5+ycFvL+qSCeseic1jS5ayttlJsdx82kjuGBqf6KjfDP+bo/wxOItPDh/I06Ph+XbKzhjfC7De6W2f3M3x+n2cMcbq/luV6X52LmobXS1eO3g7GR+f/YYjht+SEa9VeoaXdzz/jqe/3oHSsHv3lrDjGHZpCZ0LhPSnfB4hD+9t44lW8r2GaIaR8tx0xo+laCUUr9HG43RwPvo1Vo/F5HzOy77AH+3otciEeBfIvK4UqpSRHqY8wqoaP590L1XAVcBDBgwYMr27dv9kRJ21hVVc87fv6BfRiJj+qSTnhhLWmIMaQmx5jiWtIRYEuOieGbJdt7K303fHon89oxRnD62d5sloap6J797ezVv5e9mXN90Hvj+BH79Sj5ltU189OvjSIrzOZ/SLXn7u91c98IKJg/oQV5WsomLGL038ZKeGItHhLkLN/LNtgrG90vn7rPHMGlARpt+byut46ZXvmPZ9gpOG9OLm08bwXmPLWF8v3Se+fGRtoTbDn/+YB3/WrSFE0b0JCsl3iu96LSTlqh/l9U2ct+89Wwrq+fU0b2488zR9M9suxZi6dZyfv1KPrsqGrhqxmBOGtWLC/61hKuPG8Ktp48M0T88fHn8s8383/vrOWZoFr3TElv4nsXsOx7dJ73FEpSvCwyuQvf4+8787gUs6OjiUy3429fsc9ALhc0EKg+6pqI9f6ZMmSKHM7UOp5z4wCcy9U8LZG+Nw6d7vtpcKqc9tEgG3vKuXPzEEtlYXN3idZ9uKJEj71kgQ257T+Yu2ChNLreIiHy9pUwG3vKuPDh/Q8D+R1dk695aGfO7eXLuP77Y9+zawuPxyJsrdskRf1ogA295V256OV9Kqg+NU4/HI88s2SYj7/hAxv5+nrz+7U7xeDwiIvLU51tk4C3vyoI1xQH/P12Jj9fvkYG3vCu3v77Sp+sdTpf8/ZNNMvKOD2T4b9+Xv87fIPWNrkOua2hyyZ/eXSN5t74rM+77WJZuLdt37saXVsiw29+XbaW1AfsfXZHl28tlyG3vydXPLtv3XrcFXgslem++lqCWisiRSqnlwAnojg3rRCRg2Qil1F1ALfBT4HgRKVJK5QKfisiItu493NugfvVyPm+uKOR/P5nG9CHZPt/ncnt4fukOHvhwA/VNbi6bnsf1Jw8jLSH2gKqJYTkpPHjBRMb1Sz/g/l88/y0L1+7h45uOp2+PxED/rcOeRpeb8x77kp3lDbx//YwOPaPaRhePfryJpz7fSkJMNDeeMpxLjx5IbHQURVUN/ObVlSzeVMqMYdncf/54ctP3++10ezj94cW43B4+vHEm8TG+V+N2F4qrHMx+ZDE5qfG8+YtjSIj1/RkVVTXwf++v553vdC3EnWeO4rQxuhZi1a4qfvVyPptKarlk2gBunz2K5Pj9NQx7qh2c8MCnHDs0m8d/dGiG36JrbGY/spioKHj32hmkJ7ZfHaqUarEE5auB+gdwO3AR8Gu0IckXkSs6rH6/n8lAlIjUmOMFwB+Ak4Ay2d9JIlNEftOWX4ezgXp1+S5ueuU7bjh5GDec3LmG8bLaRh6Yv4EXv9lJVnI8Vx47iBeW7mBnRT1XzRjMjacMbzEB76qo56S/LuLUMb159AeT/P0rXY673l7Df7/cxr9/NJWTR/fqlB+b99byh3fWsmjjXob3SmHOxL78c9FmXG7ht2eM4pJpA1qsxlu0cS+XPbWU22eP5KqZQ/z9K10Kl9vDxU98zerdVbxz7bEM6ZnSKX++2lLGXW+vYX1xDccOzWZcv3Se+GwL2Snx3Hf++Fbbqv7+SQF/+XADz/1kGscM9T1D2R0QEX727HI+2VDCq1dPZ0L/Hj7d55eBOsijPCBNRFZ26MZD/RkMvGF+xgDPi8g9Sqks4GVgALAd3c28vC2/DlcDVVBSw1mPfsGE/uk895OjfG5Ub42Vuyr5/dtrWLGjkgGZSfz1ggkckZfZ5j0Pzt/AIx8X8MrVR7d7bXdi3upirv7fcq48dhB3njnaL79EhIXrSvjDu2vYWd7A1IEZ/PWCCQzMSm7zvh//9xuWbi3nk5uOp2dqvF8auhLN7+xDF07ge5P6+eWXy+3hf19t58EFG6l2uPjepL7cddaYNntiOpxuTn5wEclxMbx33bHERNv5Dpr57xdbueudtdxxxih+MmOwz/f5baCUUn2BgXj1/BORz3xWEEQORwPV0OTmnL9/QWltI+9fP4NeaQnt3+QDHo/w9dZyxvdLP6BqojXqm1yc+MAislPjePsXxxLlp5HsCuwsr+eMRxaTl53Mq1dP73TX5INxON18u72CaYOzfMqMbNlby6kPfcb5U/px73njA6LhcOfzTaVc+tTXnD+5H3/5/oSA+VtW28i2snqmDGy7U0szH6wq4ufPfcsfzxnLpUcNDJiOw5lVu6o477EvmTEsm39fNrVDHXxaM1A+pTyl1H3AF8AdwM1mu8nn0C2H8Id317BhTw0PXjgxYMYJICpKcfSQLJ+ME0BSXAy3nj6S1YXVvLp8V8B0HK443R6ufWEFIvC3H0wOmHECSIiNZvrQbJ9LyoN7pnD59DxeWraT1YVVAdNxuFJS4+CGl/IZ0jOFu+eMCajfWSnxPhsngFljezNtUCYPzt9AVb0zoFoOR2ocTn75wrdkpcTxwPcnBKz3qa+p7xxghIjMFpGzzHZ2QBR0ET5YVcRjn26msLKh3Wvfyi/khaU7+fnxQzo0JiNYzJnYh8kDenD/hxuocXS9xPbk51t57uvtPn1IHvhwA/k7K7nv/PEMyOrcYOhAcu1Jw8hMiuMP76ylo9XxkY7D6eaBDzfw3soiHE53m9e6PcKNL+VT2+jk7xdPDvvQCKUUvztrNFUNTuZ+tDGsWoKB2yM88tEm3lixi/qmtscuiQi3vb6KXRUNPPqDSWQkxwVMh6+xvAWIBRoDFnIXoqCkhutfzKfJ7eH+D9dz9OAszp3cj9PH9j6kJLO1tI7bX1/F1IEZ/DpCZgtQSvH7s8Yw5+9f8LdPCrjt9FHhlhQwPlhVxB/fXQvA3W+v5eTROZw3uR8zh/ck9qC2g0/Wl/Cvz7Zw6VEDI2a2gPTEWH596ghuf2MV760q4szxfcItKWA88tEm/vHpZgDSEmI4c0Ifzpvcl8kDMg7JgT/2aQFfFJRx33njGNE7MgYwj+mTzoVHDOCZJdu5ZNoAhuZEhq5A8N8vt/HgAm14k+JWc/rYXM6b3JejBmcd0gzw4jc7eXdlEb+ZNYKpAW7H9rUX32vABOAjvIyUiFwXUDWdJJxtUB6PcMG/lrCppJanf3wkizbs5fUVu9heVk9ibDSnj+3NuZP7cfSQLJxuD+c99iWFlQ28f90M+kRY1+5fv/wd73y3m/k3ziQvu+0G/MOBqnonJz+0iF5p8dxzzjjezC/k7fzdlNU1kZUcx9kT+3De5H6M6ZNGcbWD2Q8vpnd6Im9cM71D3ZaDjdsjnPno51Q3OPno18dFlLbOsmZ3FWf/7QvmTOzDuZP68dq3u5i3upgGp5u8rCTOndyP703qS//MJL7eUsYPnviKsyb0Ye6FEyNq8HJZbSPHP/ApkwZk8PQVR0SUts6ys7yeUx/6jKOHZHH1cUN4/dtdvLeyiJpGF33SE/je5L6cO7kfQ3qm7Jtg4MhBmTx9xZGdbsP2t5v5ZS25i8jTnVLjA0qpWcDDQDTwbxG5t7Vrw2mgnlmyjd+9tYYHvj+B86foHkUiwrc7Knh1eSHvrtxNjcNF77QEBmQlsXRruV/dloNJ8xiPY4Zm80QXGOPxm1e/47VvC3n7l8cwpo8eA+Z0e/ZlIhauLaHJ7WFEr1SUgh3l9bx77bEM7mS35WDy1ZYyLnr8K351ynCuO2lYuOX4hcvt4Zx/fEFxVSMLfzWTHkm6Sqi20cW81cW8tnwXS7aUAXDkoEy2l9WRGBvNu9fNIMXHttVQ8u/FW/jTe+t46vKpnDgy8tJ1RxARfvTUUlbsqGT+jTP3ZaIdTjcL1u7htW938dnGvXgEJvTvQWV9E/VNbj64fgbZKZ3vaRqIXnxxQHOd1AYRCVpjhVIqGtgInALsAr4BfiAia1u6PlwGqrCygVMfXMTkgRmtTkvjcLr5aF0Jr3+7i0837uXKYwdx++zIrUJrHuPxvyunceyww3eMxxcFpVzy76/5+fFDuGVWy+PJq+qdvLNyN69/u4sVOyuZe+HEiJ6b8JrnlvPJ+r18fNNxBwzsPdz416LN/PmD9fz94smcMb7lqtRdFfW8lb+b15bvoqjKwStXH83YvuktXhtumlweZs3VHZrn3TAzoB1rQk3zuMw/zhnDpUfntXhNSY2Dt/N38+ryXWzeq2uOOjLBQEv4W4I6Hj1x6zZAAf2By4LVzVwpdTRwl4icZn7fBiAif27p+nAYKBHhx//9hq+2lDP/xpntzusFukt3Ymx0RFcDOJxuTnloEYmx0bx/3YzDcoxHQ5Ob0+Z+RnSU4oPrZ/hUJVbf5Ap7w3t77Cyv56QHFzF7bG/mXnR4DqzeVlrHaXM/Y+bwnjx+6ZR204KI4HB6OjQpcjj4eP0efvzfZR0e/xNJ7K1p5OQHFzG8VwovXXW0T9V1gUo3fnUzB/4KnCoix4nITOA04CG/VbVOX2Cn1+9dxm0fSqmrlFLLlFLL9u7dG0QpLfP2d7v5ZMNebjpthE/GCXSX7kg2TqC7Qv929ig27qnlrnfWUFnfFG5JHebBBRvYUV7PveeO87m9JtKNE0D/zCSumjGYN/N38+TnW2l0td3zLdJo7u0VFx3FH+eM9SktKKUi3jgBnDAih+OG9+ThhZuYt7r4sOxxedc7a2hocvPnc8f73JYU7HTjq4GKFZENzT9EZCO6V1/YEJHHRWSqiEzt2TO0XbXL65q4+521TOzfg8un54U07FBw2pjeXDxtAP/7agcz7vuEhxduOmy6n3+3s5InP9/KxdMGMG1wVrjlBJyfHz+Eowdn8cd313LiA4t4cekOnG5PuGX5xEvf7GTJljJumz2K3umBG/sXCSiluPvsMfRMi+fq/y3nrL99zifrSw4bQ7Vg7R7eW1nEdScNZWhO5LTB+lrF9xTgAf5nnH6Inkfvx0ERFeFVfDe+lM873+3mvetmREyX12CwrqiahxZsZP7aPfRIiuVnM4dw2fSBEVvacLo9nPXo51TUN7HgV8eR1kXX7BERPi8o5YH5G/luZyUDs5K4/qRhzJnY1+/psoJFSbWDkx5cxOjcNF746VFddsYSl9vDm/m7efijjewsb2DSgB7cdOoIpg/Jitjak2qHk1MeXERGUhzvXHvsIcMvQoG/bVDxwC+AY43TYuAfIhKUcVFKqRh0J4mTgEJ0J4mLRWRNS9eH0kB9uqGEy//zDdedOJRfndrmJOtdhpW7KnlwwUY+3bCX7JQ4fn78UC6ZNiDiujs3d/B4/NIpnDqmd7jlBB0R4aN1Jfx1wUbWFVUzNCeFG04exuyxuRFnAK5+djkfbyhh3vUzIrKXZKBpcnl4ZflO/vZxAUVVDo4anMmvTx0RkfNd/vaNVbywdAdvXHOMz5O7BppAThabCfTzd7JYH8KZDcxFdzN/SkTuae3aUBmo2kYXpz30GYlx0bx33bHdbhmE5dvL+ev8jXy5uYzeaQlcfkweQ3um0Ds9gdz0BDKT48KWS9y8t5bTH17MKaN68fdLJodFQ7jweIR5a4p5cMFGCkpqGdk7lR8eNZD+mUnkpifQOz2B1PjwtX/OW13E1f/7lt/MGsE1xw8Ni4Zw4XC6eWHpDv7+yWZKaxuZObwnZ0/oQx8TL7npiWFtY/t6SxkXPv4VPzl2EHf4OSmyP/hbgvoUOBs988RyoAT4UkRuDLDOTpE3cpxsW78q6OHc9fYanl6yjVevPpopAyMvJxQqvtxcyoPzN7Jse8UB7nExUfqDmJZAnx6J9E5PYNaY3kHPlXk8woWPL2HjnloW/uq4bjvzt9sjvPPdbuYu3Mi2svoDziXHRZPbI3Ff/OT2SOTCI/oHfR2w5sHSPVPieeuXx4Sl+igSaGhy88ySbfxz0WYqDppyq0dSrI6TdB0vAzKTuGTagKAvK+9wupn98GKcHg8f3jAzrFX3/hqoFSIySSn1E6C/iPxeKbVSRCJiiuX43GHy2RdfBbVRfPn2Cs7/55f86KiB3D1nbNDCOVwQEfbWNLK7ykFxVQNFVY59W/Pv4ioHiXHRLPzVcQGdEPdgnv1qO3e+uZq/nD+e70/tH7RwDhfcHqGoqoHiKscB8eP9u6SmkRG9Unn7l8cGddzOLa+u5NVvd/HWL46J2HFMoaTJ5aGwsmFf/Og0433soLyuiXMm9gn6UIL7563nH59ujogxj60ZKF9NZoxZ3fYC4LcBVRYA4qKjuPX1VT6PeekoNQ4nt7y2kty0BG5uZdBnd0MpRU5aAjlpCdBKCWlraR2z5n7GHW+u9mnMS2fYXlbHfR+sZ8aw7H0zeXR3oqMU/TKS6JfR+vCHhWv38JNnlvGvRZu5NkgzU3yyoYSXlu3kZ8cNtsbJEBcTxaDsZAa1MZXYQws28vBHmzh7Yp+gzUyxYkcF//psC9+f0i/sxqktfM06/QH4ECgQkW/MYoObgierY/TNSGRraR1zFwZe0vLt5cx+ZDFb9tby5/PGR+RUK5HKoOxkfnXKcBas3cP7q4oD7v9b+YWc+cjnKAX/971xEdtLKhI5eXQvzprQh0c/LmDTnpqA+u1ye3h44SZ+8vQyhuakcGMnV4rurlxzwhCG90rht2+sDvjwDo9HeOrzrVz4+FfkpMZzxxnha3fyBZ8MlIi8IiLjReQa83uLiJwXXGm+kxIfw4VT+/PE4i2s2hWYdXNcbg8PLtjI9/+5BIBXrj46IpbGONy48thBjOubzu/fXk1FXWAG/VY7nFz/4gqufzGfEb1Tef+6GT4Plrbs5/dnjSY5PppbXluJ2xOY8To7yuq58PGveGjhRs4an8vrETbx7uFAfEw09503nuJqB/fNWx8wf0uqHVz+32/4w7trmTksm3euPbbNlYMjAV8XLExQSv1CKfUPpdRTzVtnA1VK3aWUKlRK5Ztttte525RSBUqpDUqp03z18/YzRpGVHMdvXlvp98DF7WV1nP/PJTzy0SbOmdSX96+b0a07RfhDTHQU9503nsp6J398r8WpFDvE0q3lnD53Me+uLOJXpwznxauOssapk2SnxPP7s8bw7Y5KnlmyzS+/RITXlu9i9iOL2Vhcw8MXTWTuRZO67Fi0YDNpQAY/PmYQ//tqB1+biXP9YcHaPcx6eDFLt5bxp3PG8sSPpvo1uWuo8LWK71mgN3qKo0VAP8DfeoGHRGSi2d4HUEqNBi4CxgCzgH+YiWPbJT0xlj+eM5Z1RdU8/tmWTgkSEV5ZtpPZD+sqvUd/MIkHL5gY9N40XZ3RfdL4+fFDeP3bQj7dUNIpP5xuDw98uIGLHl9CTLTi1auP5rqThh2WcwVGEnMm9uGEET25f94GdpbXt39DC1TVO7n2hRX8+pXvGJ2bxgc3zIjoSXcPF3596nAGZCZx6+ur2l3QsTXqm1zc/sYqfvrMMnLTE3j32hn88KiBh011uK+pe6iI3AnUmSU2zgCmBUHPHOBFEWkUka1AAXCkrzefNqY3Z4zL5eGFmygoqe1QwJX1Tfzi+W+5+dWVjO2bzrwbZnLWhK6zOFy4+eWJegqV376xmtrGtlfoPJitpXWc/9iX/O2TAs6b3I/3rpvBpAG+L89taR2lFPd8bxxRCm5/Y1WHp+ZZsrmM0x/+jHmri7n5tBG8cNVRbXbOsPhOUlwM9547jq2ldTy0sOOr9q7aVcWZj37OC0t38LPjBvPGNcdE1DRGvuCrgWpuqatUSo0F0oEcP8P+pVJqpakubP7atDtJbDOtTRZ719ljSIzT9eoeH+vVvywoZdbcxcxfs4dbZo3k+Z8eFXGLCR7uNNer765q4H4f69VFhJe+2cEZjyxmW1k9/7hkMn/5/gTbUSXA9OmRyK2zR7F4UymvLN/l0z1NLg/3zVvPxf/+ivjYaF77+XR+ccLQiJ1q6XBl+tBsLjqiP098toWVuyp9usftER77dDPf+8cX1De6ee4n07jt9FGH5TIgvip+3BiRO4G3gbXA/W3doJRaqJRa3cI2B3gMGAJMBIrQs6V3iNYmi+2ZGs/vzhzN8u0VPPvV9jb9qG9y8fu3VnPxv78mKS6aN645hp8fP8QmsiAxZWAGl0/P45kl21m6tbzNa/dUO/jxf7/hltdWMaFfD+bdMCNilmHvilxy5ACOzMvkT++upaTa0ea1qwurOPtvn/PYp5u5cGp/3r322LBNkdMduG32KLJT4vnNq+23r28oruG8x77kvnnrOW1Mb+bdMMPvtZrCSYenOgq4AKXygHdFZOzBk8IqpT5ETxq7pC0/Dp7qSES47D/fsGybXquppSqHpVvLuemV79hRXs/l0/P4zawRETsJaleirtHFaXM/Iy46ivdbGLcmIryxopC73l5Dk9vDzaeN5IrpeRE3t1xXZIuZLuqEETn889Iph5x3uj38/ZMC/vZxARnJcfzf98ZxSgSuDN0Vmb+mmKueXc5Npw7nlyceOm6tyeXhH58W8PdPCkhNiOX3Z43m7Al9Dpu2Jr/Wg1JK9VJKPamU+sD8Hq2UutIPMd5Z4e8Bq83x28BFSql4pdQgYBiwtBP+83/fG4sCbn9j9QH16g1Nbv7wzloufFzbvBevOoq7zh5jjVOISI6P4c/njmNLaR2PfHTguLWSGgc/fWY5v3r5O4b10t3Hrzx2kDVOIWJwzxRuPGU489YU88GqogPOrSuq5py/f8HchZs4Y3wu82+YaY1TCDl1TG/OGJ/LIx8VUFByYP+0FTsqOPPRxTpuxuWy8FfHMWdi38PGOLWFr1V8/0UP1G3uNbARuMGPcO9XSq1SSq0ETgBuBDCzlb+MrkKcB/xCRDrVfaVfRhK3nD6Szzbu5fVvCwFYtk0Pun3qi61cetRAPrh+Bkd1wTWDIp0Zw3ry/Sn9+NdnW1hdWIWI8FZ+Iac+9BmLN+3ljjNG8fLPju4Ws15HGj85dhBj+6Zx51t6sUqX28PfPt7E2X/7nD3VDv75wyk8fNEkMpLjwi2123HXWWNIio/mN6/qcWv1TS7++O5azn3sS2ocLp66fCpzL5pEZheKG1/n4vtGRI5onpPPuOWLyMRgC/SF1mYz93iEC/61hE0ltXxvUl+eXrKNvj0Suf/88Yd1vWxXwHsS0QGZScxbU8ykAT144PsTGGINU1hZu7uas//2OcePyGFPtYNVhVWcNaEPd589pkt9/A5H3lixixtf+o4fHDmAzwv2srO8gR8eNYBbZo08rIfD+DsXX51SKgsQ49lRQGCmbAgiUVGK+84fz+kPL+a/X27jkmkDuG32KNsLLAJIT4rlj3PGcvX/llOwt5bbTh/JT2YMth1UIoDRfdK4+rgh/O2TArKS43jsksmcbjuoRATnTOzLW/m7eWHpDgZlJ/PSVUd1yZWjm/G1BDUZeBQYi24v6gmcH+w1oXylvfWgviwoJSY6iiMH2dkgIo13vtvNqNy0w258Rlen0eXmjW8LOWV0L7IOgxkHuhNltY0sWLuHcyb17TLTSHVquQ2l1BHAThEpNqvc/gw4D91G9DsRabuvcIhQStUAG8KtA8gGSsMtwhApWqyOA4kUHRA5WqyOQ4kULaHSMVBEDpnstD0D9S1wsoiUK6VmAi8C16LHL40SkfODJLZDKKWWtWR9u6sOiBwtVkdk6oDI0WJ1HEqkaAm3jvYaY6K9SkkXAo+LyGvAa0qp/KAqs1gsFku3pr1u5tGmag/gJOBjr3O2p4HFYrFYgkZ7RuYFYJFSqhRoABYDKKWGElm9+B4PtwBDpOiAyNFidRxIpOiAyNFidRxKpGgJq452e/GZLuW5wHwRqTNuw4EUEfk2+BItFovF0h0J+1x8FovFYrG0xOE3/7rFYrFYugciEvIN6A98gh5PtQa43rhnAguATWafYdxHAkuARuAmL39GAPleWzVwQythzkKPlSoAbvXSsRFoQs+ScXs4dBj3l9HtfA6zvz+Mz2SFl5al6LbKDukw52408bsa3Z6Z0IqOy4y/m4DLvHRsQ69F5u7sOxIgHRXG3wb0GmU5odZh3K8Bak28lPr5TK43Ota09n4EO90ESEeR0dEcNxNDrcO4X4heZdwBVAI3tvNMLgFWAquAL4EJ7YURpHQTFB3G/VNzf77Zclrzo1W/O3pDIDZ0m9Zkc5xqXvbR6DWmml++W4H7zHEOcARwz8Evlpef0UAxesBXS+c2A4OBOOA7E14u8AMgD9huIiLkOsy5l4CbI+CZ9DH3DDc6yoA7OqoDvdDkViDR/H4ZuLwFHZnAFrPPMMcZJm4uM/vazj6PAOlYBkz1J14CoCML/QE+0Vz3HHpxz85oaZ4RJgmd+ViIXjU7pOkmQDreBs73M2781REFFALnmOvuRaehtrRMZ7+ROB34ur1vRJDSTVB0mHOfAlP9sRVhqeITkSIxHSxEpAZYh07Ac4CnzWVPA+eYa0pE5Bv2r+zbEicBm0WkpVUKjwQKRGSLiDShBxzPMTpeEJFt6JzgpnDoMOca0B+wsD4T41+diGw0OtYCZ3VSRwyQaIYqJAG7W7jmNGCBiJSLSAU6dzfLxM3TItK87oM/z8MvHeiEHoh46bQO9Idig4g0D/WYB7g6qWUU+kNULyIuYBFwbgtagp1u/NYBlJsw/Ikbv3SgMw8OEXnTXPceoNrR8qWJX4CvgH7thHEwgUo3QdHRwnWdIuxtUGbBwknA10AvrwdbDHRkwZmL0NUmLeHLUvIxwLgw67hHKbVSKfUk4XsmpUCMUmqqiZsJQEpHdYhIIfAAsANdDVMlIvM7oMMbRSefRwB1/EcptRY4Lkw6CoARSqk8Y+AuNmF25h1ZDcxQSmUppZKA2eiqIV+1eONPugmUjnuUUuvQS/esCIOOfWnGuF+BLk34+kyuBD7w8b+2p8WbjqabYOj4j1IqXyl1p+rEAlVhNVBKqRTgNXSdb7X3OdFlRPHRnzjgbOAVP3T0BH4bRh23oevIj0fnbr4IhxYTxkXAw+j6+A85KMfpiw6lVAY6xzUIXW2YrJT6YUe0GH9SgAQ6+Y4ESMclwNHotottmNxnKHWY3OnP0VXBXwCTgVWdeSYisg64D5iPLonlo9srOoS/6SZAOm4DpqBrIJaj2+lCqsMrzTyklFqGrior9OWZKKVOQBuGW3wNzxc6mm6CpOMSERkHzDDbpR31IGwGSikVizZOz4nI68Z5T/Nqu2Zf4qN3pwPfisgec29/Y7XzlVJXo+uHvXNE/Yybt446dNE8LDpM7iYGXeJ5EV2nHhYt6DaXWuBO4Al0PXZHdZwMbBWRvSLiBF4HpiulpnnpOLstHV5x4/LjHfFbhwnjNeBZYC66+iPkOkTkHeBYdCP8IrN15pkgIk+KyBQRmYnuBLIxHOnGXx3o0strwP+Au+lc3PitQ0SWACei22zfAr5p75kopcYD/0ZXV5YZ5xbDCGa6CZYOU2vQXP36PDpuOob40YDV2Q1d9HwGmHuQ+184sCHv/oPO30ULHQLQH/Qr2ggvBt14N4j9DX5jvHWgc8bZ4dBhzuV6aZkL3BvGZ/Ky0RAPfIROeB3SAUxDl8CSjJ9PA9e2oCMT3faWYbatxs07bmo7+44EQEcMutQyF4gFXgWuDrUOcy7HPJPH0Ln84Z19RzA9qoABwHqgRzjSTQB0vGJ0KLO/N9Q6DoqbRzFppq1nYsIpAKb7GkYw0k0QdcR4vRf70k1r36NWv1MdvSEQGzoXKOjujflmm41ubPwI3ei6kP0Jsze6brManXvcBaSZc8noXEt6O2HORpcENqOrJLx1NHeXdaKL+CHVYdyXGw0OdMPvyjA/E4fZCv3QcTc6sa9Glz7iW9HxY3QiKcAY1Rbipgndcy3UOk424TeYbS9wZqh1GPcFXnGzDf/SzWJ0B5jvgJPCmG4CoaM5bsrRnRtCqsO4P+8VN4U+xM2/0SW1fLMtay+MIKWbYOlIRn/TVqIzZg+jJx/vkK2wM0lYLBaLJSIJey8+i8VisVhawhooi8VisUQk1kBZLBaLJSKxBspisVgsEYk1UBaLxWKJSKyBslgsFktEYg2UxWKxWCISa6AsFovFEpFYA2WxWCyWiMQaKIvFYrFEJNZAWSwWiyUiiQm3gECQnZ0teXl54ZZh6YJU1jsprGzA4zVnZZRS9O2RSI+k2DAqCy6V9U6Kqx043R5io6PonZbQpf+vJbwsX768VER6HuzeJQxUXl4ey5YtC7cMSxfkmHs/xlXZcIh7rx6JfHHriWFQFHzeXFHIba+vItu5f82+2Nho7jh3HOdMamlRVYvFP5RS21tyt1V8Fksb7G7BOLXl3hX4y4cbaPAyTgANTjd/+XBDmBRZuivWQFksbdCnR2KH3LsC3dEoWyITa6Aslja4+bQRJMZGH+CWGBvNzaeNCJOi4NMdjbIlMrEGymJpg3Mm9eXP546jb49EFNC3RyJ/7uJtMd3RKFsiky7RScJiCSbnTOrbpQ3SwTT/1798uIHdlQ306ZHIzaeN6FbPwBIZWANlsVgOobsZZUtk0qaBUkpltnVeRMoDK8di8Z03VxTaXL7F0oVprwS1HBBAAQOACnPcA9gBDAqmOIulNZrH6jR3hy6sbOC211cBWCNliUhshqrjtNlJQkQGichgYCFwlohki0gWcCYwPxQCLZaWsGN1LIcTzRmqwsoGhP0ZqjdXFIZbWkTjaxvUUSLy0+YfIvKBUur+IGmytEJ3y4G19X8DOVYnUp5rqHREyv/tTrSVobLPvnV8NVC7lVJ3AP8zvy8BdgdHkqUluluVVnv/t0+PRApbMEYdHasTqOfq70c/VPHb3d6jSMEOfu4cvo6D+gHQE3jDbDnGLWgopWYppTYopQqUUrcGM6zDge5WpdXe/w3UWJ1APNdAVN8EKn7fXFHIMfd+zKBb3+OYez8+REN3e48iBTv4uXP4VIIyvfWuD7KWfSilooG/A6cAu4BvlFJvi8jaUGmINAKVAztcqnfa+7++jtVp7/8G4rkGovomEDp8KR3ZnHx4uPm0EQfEDdjBz76gxGsZgUNOKjVXRG5QSr2D7s13ACJydlBEKXU0cJeInGZ+32bC+3NL16empsqUKVOCISVi2DnpKtzx6Ye4RzdW0X/F4z75UZs1krLBs5Do/csmKLeTrC3zSClbf8B1FQNm4o5LI7qpmowdnx1wPhSE6v8GIpxt024CpQ49IULe1w/45EcgdPjiRyDCsXSOSEhXkcqiRYuWi8jUg93bK0E9a/a+pbLA0RfY6fV7FzDN+wKl1FXAVQDx8fGhUxYmMnZ81uLHNmPHZwdc11YiqBgw84D7ASQ6looBM/ddc/BH3R2fTtngWQAhTUy+/t+28OX/BiKc6Kbqlj/6TdU++xEIHe64tHbdAxFOVyNUhiOlbL01SB2kzRJUizcolQH0F5GVwZEESqnzgVki8hPz+1Jgmoj8sqXrp06dKt1hPaj2qqsOruIBXY3QPHfcoFvfO7QYjB7YtvXeMwC9/lFLnQ/6BmH9I1/+jz/Vkb7830CE095z74g//ujwNe4Ol2reUBCouLP4h1KqUyWo5ps/Bc421y8HSpRSX4jIrwKqcj+FQH+v3/2MW7emveln2msL8aXnW6jaKHxpL/F3uh1fe/r5G44v7WG+GAVfdLTlj6/tHHYao/0Eqvt3oIy+zTwciK/dzNNFpFop9RPgGRH5vVIqaCUo4BtgmFJqENowXQRcHMTwwk4gXsz2jIsvH7BAdd9uj1CMCwllw3RbH/1AdmVvyx87yWvHCVXnlFD605Xw1UDFKKVygQuA3wZRDwAi4lJK/RL4EIgGnhKRNcEON1wE6sVsz7j48gEL1Uc9FCW1SPlgB8oY++LP4VQ6ioTSQiAyZKGM3+6GrwbqD2hj8YWIfKOUGgxsCp4sEJH3gfeDGUakEKgX0xfj0t4HLFAf9fY+PqEqqUXCBztQxrgrdRGPlNJCIDJkNn6Dh6/joF4BXvH6vQU4L1iiuhuBejEDZVz8/aj78vHpTuNCAmWMQ2XUQ4EvmbJQlLACkWZs/AYPXztJDAceA3qJyFil1HjgbBH5U1DVdRMC+WJGQonB16qo5mu7entJoIxxpBj1ULSXhrKE5W+a6WrxG0n4WsX3BHAz8C8AEVmplHoesAYqAHS1F9PXEmEkGNNQEMiSbSD88YdQtZceTu0xXSl+Iw1fDVSSiCxVB46WdwVBT7ekq72YtqriUAJljMNt1H01HO2VstrLlEVae0x7/6erxG+k4auBKlVKDcFMd2QG0hYFTVU3pCu9mF2tRGjZjy+Gw9cxbtB6piySMjmR0qGjO+KrgfoF8DgwUilVCGxFL7lhsRxCVysRWvbji+HwtZTVVqYskJkcf9vMDqfqxq6Gr734tgAnK6WS0Ut01KMHz24PojZLhBKoWREshx++GI5AVM8FcriDv6WfSKtu7E60aaCUUmno0lNf4C300u+/AH4NrASeC7ZAS2Rhqzu6N74YjkBVzwUikxOI0k8kVTd2N3yZzbwCWAL8FD2LhAK+JyL5wZVmiURsdYelPcMRSW2QgSj9RNL/6W60Z6AGi8g4AKXUv9EdIwaIiCPoyiwRia3usLRHJLVBBqL0E0n/p7vRnoFyNh+IiFsptSsQxkkpdRe6RLbXON1upjZqXpzwSsANXCciH/obniVw2OoOiy+Eqg3S3+7svmLbVMNDewZqglKqedU1BSSa3woQEWl5hTTfeEhEDlgIUSk1Gt35YgzQB1iolBouIu6WPLCEHlvdYYkUAtGd/XAjEibYDSVtGigRiQ6VEMMc4EURaQS2KqUKgCPRbWCWCKCrJXjL4UsgurMfTnTHDkq+joMKBr9USv0IWAb8WkQq0L0Fv/K6ZpdxOwTvJd8HDBgQZKkWb7pKgrcc3nS39tDu2EEpKlgeK6UWKqVWt7DNQU88OwSYiO548deO+i8ij4vIVBGZ2rNnz8CKt1gsEU9r7Z5dtT20uxlkCGIJSkRO9uU6pdQTwLvmp13q3WKx+ER3aw/tjh2UlIiEPlClckWkyBzfCEwTkYuUUmOA59HtTn2Aj4Bh7XWSUErVABuCLNsXsoHScIswRIoWq+NAIkUHRI6WTuuISkzLjE7J7KuiY+LE7Wpy15YXehqqy0OtIwgcoiUqMS0zJq3nQJTaX/Ml4nFV793ux3/usI4gMVBEDqkKC1cb1P1KqYnoyWe3AT8DEJE1SqmXgbXo2dJ/4WMPvg0iMjVIWn1GKbUsEnRA5GixOiJTB0SOFqvjUCJFS7h1hMVAicilbZy7B7gnhHIsFovFEoEErZOExWKxWCz+0FUM1OPhFmCIFB0QOVqsjgOJFB0QOVqsjkOJFC1h1RGWThIWi8VisbRHVylBWSwWi6WLYQ2UxWKxWCITEQn5hh6M+wm6O/ka4HrjngksADaZfYZxH4mej68RuMnLnxFAvtdWDdzQSpiz0GOlCoBbvXRsBJrQXd5vD4cO4/4y0AA4zP7+MD6TFV5alqJ7e3ZIhzl3o4nf1cALQEIrOi4z/m4CLvPSsQ09o767s+9IgHRUGH8b0NNv5YRah3G/Bqg18VLq5zO53uhY09r7Eex0EyAdRUZHc9xMDLUO434hUGPiphK4sZ1ncgl60ddVwJfAhPbCCFK6CYoO4/6puT/fbDmt+dGq3x29IRAbkAtMNsep5mUfDdzv9fLdCtxnjnOAI9Ddz29qxc9ooBg94Kulc5uBwUAc8J0JLxf4AZCHXr6+IBw6zLmXgJsj4Jn0MfcMNzrKgDs6qgM9h+JWINH8fhm4vAUdmcAWs88wxxkmbi4z+9rOPo8A6VgGTPUnXgKgIwv9AT7RXPccsLOTWsaiP8ZJ6MzHQmBoqNNNgHS8DZzvZ9z4qyMKPePNOea6e9FpqC0t09lvJE4Hvm7vGxGkdBMUHebcp8BUf2xFWKr4RKRIRL41xzXAOnQCngM8bS57GjjHXFMiIt/gtT5VC5wEbBaR7S2cOxIoEJEtItIEvAjMMTpeEJFt6JzgpnDoMOca0B+wsD4T41+diGw0OtYCZ3VSRwx6iZYYdOLf3cI1pwELRKRc9ITBC4BZJm6eFjPjiJ/Pwy8d6IQeiHjptA70h2KDiHxsrpuHHszeGS2j0B+iehFxAYuAc1vQEux047cOoNyE4U/c+KUDnXlwiMib5rr30EsStaXlSxO/oCfI7tdOGAcTqHQTFB0tXNcpwt4GpZTKAyYBXwO9vB5sMdCrA15dhK42aYm+6NxmMy3Nkh4DjAuzjnuUUiuVUk8SvmdSCsQopaaauJkApHRUh4gUAg8AO9DVMFUiMr8DOrxRdPJ5BFDHf5RSa4HjwqSjABihlMozBu5iE2Zn3pHVwAylVJZSKgmYzYFzYLanxRt/0k2gdNyjlFoHnICung61jn1pxrhfgS5N+PpMrgQ+8PG/tqfFm46mm2Do+I9SKl8pdadSSrVwf5uE1UAppVKA19B1vtXe50SXEcVHf+KAs4FX/NDRE/htGHXchq4jPx6du/kiHFpMGBcBD6Pr4z/koBynLzqUUhnoHNcgdLVhslLqhx3RYvxJARLo5DsSIB2XAEej2y62YXKfodRhcqc/R1cFfwFMBlZ15pmIyDrgPmA+uiSWj26v6BD+ppsA6bgNmIKugViObqcLqQ6vNPOQUmoZuqqs0JdnopQ6AW0YbvE1PF/oaLoJko5LRGQcMMNsrc4g1BphM1BKqVi0cXpORF43znuUUrnmfC5Q4qN3pwPfisgec29/Y7XzlVJX08Ys6V466tBF87DoMLmbGHSJ50V0nXpYtKDbXGqBO4En0PXYHdVxMrBVRPaKiBN4HZiulJrmpePstnR4xY3Lj3fEbx0mjNeAZ4G56OqPkOsQkXeAY9GN8IvM1plngog8KSJTRGQmuhPIxnCkG391oEsvrwH/A+6mc3Hjtw4RWQKciG6zfQv4pr1nopQaD/wbXV1ZZpxbDCOY6SZYOkytQXP1a/Mk4B1D/GjA6uyGLno+A8w9yP0vHNiQd/9B5++ihQ4B6A/6FW2EF4NuvBvE/ga/Md460Dnj7HDoMOdyvbTMBe4N4zN52WiIR88of2JHdQDT0CWwJOPn08C1LejIRLe9ZZhtq3Hzjpvazr4jAdARgy61zAVigVeBq0Otw5zLMc/kMXQuf3hn3xFMjypgALAe6BGOdBMAHa8YHcrs7w21joPi5lFMmmnrmZhwCoDpvoYRjHQTRB0xXu/FvnTT2veo1e9UR28IxIbOBQq6e2O+2WajGxs/Qje6LmR/wuyNrtusRucedwFp5lwyOteS3k6Ys9Elgc3oKglvHc3dZZ3oIn5IdRj35UaDA93wuzLMz8RhtkI/dNyNTuyr0aWP+FZ0/BidSAowRrWFuGlC91wLtY6TTfgNZtsLnBlqHcZ9gVfcbMO/dLMY3QHmO+CkMKabQOhojptydOeGkOow7s97xU2hD3Hzb3RJLd9sy9oLI0jpJlg6ktHftJXojNnDQHRHbYWd6shisVgsEUnYe/FZLBaLxdIS1kBZLBaLJSKxBspisVgsEYk1UBaLxWKJSKyBslgsFktEYg2UxWKxWCISa6AsFovFEpH8P7RlEwvu+0HmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test for stationarity / unit roots (Augmented Dickey Fuller Test)\n",
    "# Arima models needs the input data to be stationary, so p-value need to be < 0.05\n",
    "print_adf_test(y_train)\n",
    "\n",
    "plot_decomposition(y_train, period=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ADF test ---\n",
      "ADF Statistic: -3.669 \n",
      "p-value: 0.005\n"
     ]
    }
   ],
   "source": [
    "# With the first difference the data is stationary\n",
    "y_diff = y_train.to_numpy()[:-1] - y_train.to_numpy()[1:]\n",
    "print_adf_test(y_diff[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ACF and PACF plots to have an idea of p and q parameters of ARIMA model\n",
    "tsplot(y_diff, lags=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(2,2,2)(0,0,0)[0]             : AIC=489.450, Time=0.09 sec\n",
      " ARIMA(0,2,0)(0,0,0)[0]             : AIC=490.982, Time=0.01 sec\n",
      " ARIMA(1,2,0)(0,0,0)[0]             : AIC=491.917, Time=0.02 sec\n",
      " ARIMA(0,2,1)(0,0,0)[0]             : AIC=489.446, Time=0.02 sec\n",
      " ARIMA(1,2,1)(0,0,0)[0]             : AIC=485.949, Time=0.04 sec\n",
      " ARIMA(2,2,1)(0,0,0)[0]             : AIC=487.006, Time=0.05 sec\n",
      " ARIMA(1,2,2)(0,0,0)[0]             : AIC=487.422, Time=0.04 sec\n",
      " ARIMA(0,2,2)(0,0,0)[0]             : AIC=485.995, Time=0.02 sec\n",
      " ARIMA(2,2,0)(0,0,0)[0]             : AIC=492.007, Time=0.02 sec\n",
      " ARIMA(1,2,1)(0,0,0)[0] intercept   : AIC=inf, Time=0.07 sec\n",
      "\n",
      "Best model:  ARIMA(1,2,1)(0,0,0)[0]          \n",
      "Total fit time: 0.384 seconds\n"
     ]
    }
   ],
   "source": [
    "# Run and auto ARIMA model, it test different parameters and select the best one\n",
    "# in terms of Akaike Information Criteria (AIC)\n",
    "\n",
    "m1 = StatisticalModel(\"auto_arima\")\n",
    "m1.auto_arima(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>44</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 2, 1)</td> <th>  Log Likelihood     </th> <td>-239.975</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sun, 09 Jan 2022</td> <th>  AIC                </th>  <td>485.949</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:43:46</td>     <th>  BIC                </th>  <td>491.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>487.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                       <td> - 44</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>ar.L1</th>  <td>    0.4668</td> <td>    0.266</td> <td>    1.756</td> <td> 0.079</td> <td>   -0.054</td> <td>    0.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.9187</td> <td>    0.216</td> <td>   -4.248</td> <td> 0.000</td> <td>   -1.343</td> <td>   -0.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 5256.4237</td> <td>  896.611</td> <td>    5.863</td> <td> 0.000</td> <td> 3499.098</td> <td> 7013.750</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.04</td> <th>  Jarque-Bera (JB):  </th> <td>17.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.85</td> <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.56</td> <th>  Skew:              </th> <td>1.17</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.09</td> <th>  Kurtosis:          </th> <td>5.11</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                   44\n",
       "Model:               SARIMAX(1, 2, 1)   Log Likelihood                -239.975\n",
       "Date:                Sun, 09 Jan 2022   AIC                            485.949\n",
       "Time:                        21:43:46   BIC                            491.162\n",
       "Sample:                             0   HQIC                           487.860\n",
       "                                 - 44                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.4668      0.266      1.756      0.079      -0.054       0.988\n",
       "ma.L1         -0.9187      0.216     -4.248      0.000      -1.343      -0.495\n",
       "sigma2      5256.4237    896.611      5.863      0.000    3499.098    7013.750\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.04   Jarque-Bera (JB):                17.28\n",
       "Prob(Q):                              0.85   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               2.56   Skew:                             1.17\n",
       "Prob(H) (two-sided):                  0.09   Kurtosis:                         5.11\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = m1.fit_auto_arima(y_train)\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 329.43\n",
      "MAPE: 32.44%\n"
     ]
    },
    {
     "data": {
      "image/png": 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DaktMo4h54bgtFldHd48SN6QwkyqLC1QfCevq1RHVR/yFd2VhLclnK2hgKgjMAABkiIM9/clAnNjgo21/XG8fPp7ssyDXVFMa0tnLC/Wh85arvrxQ9ZGwaiMh5S9g4R0wEwjMAADMIuec9h3pSy66S90SurunP9lv8YJc1ZWFdKl//+K6iDe/uLK4gK2ggVlGYAYAYAb0DQ6po7tX7bG42mI9aovF1R7rUdv+uI72jWwFvSQ/Tw3lhXr/2eVqKPemUNRH2AoayCQEZgAAJsk5p654v9pjcbV3eWG4vcsLx3sO9CplwzudsSRftZGQbrlwpRrKw8nbtUXCbAUNZDoCMwAAJ9E/OKyO7p7RleJYXO2xuI4cH6kWL8rLUU1pSOesXKqbz1+hurKwakvDqomEFGbHOyBr8ekFAEAju921x3r8aRQjwXjPwWMaSikXly9ZpLpIWB++YIXqImHVRsKqLQ0xjQKYowjMAIB5pX9wWLsPjFct7tHhYyO73SWqxWtXLNWHzk8E45BqSkMq5DZtwLxCYAYAzDnOOR3o6R89r9j/vvtA76hqcVmhVy1ef97yZCiuY9EdgBQEZgBA1hoYGlZHd2+yQpycStHVo0O9I9XihXk5qvXvXbz+vOXJUEy1GMCpIDADADLegZ7+E+YVt8d61DFOtbg2EtJN545Ui+sjYa1Ytli5VIsBTBKBGQCQEQaGhrX7QO8JUyjaYvHR1eJcb27xWWcU6qZzU6rFkRBbQAOYEQRmAMCsOtjTP1Ip7oqrbX+P2rvi2t3dq8GUanGkcJFqS71qcW2pF4rrImGtLKJaDGB2EZgBANMuUS0e7xZtB8dUi6tLC3RmWaFuPOcM1ZZ6O93VUi0GkEEIzACASTvU61WLx96ibWy1uDTszS2+4ZzlqvOnUNRGQqooKqBaDCDjEZgBAIEGU6rFybtRdHkh+UBPf7LfwtwcVZV41eIb1p6h2khYdZGQaiNhLV1MtRhA9iIwAwAkJarFiSkUI1Mpdh/o1cBQarV4oWpLw7p+bbk/hSKk2tKwKooWKy83J42/AQDMDAIzAMwjg0PD2nPw2Li3aOtOqRYvyDVVlYRUXxbWB9aeMbKhR2lYSwuoFgOYXwjMADAHHe4d8O9A4d2aLVE17ujuGVUtLgktVF0krOvWlCdvz1YbCWsV1WIASCIwA0CWGhwaVufBY958Yv/WbInvXfGRanFejqmqpEB1kbDef3Z5cl5xXSSkZQUL0/gbAEB2IDADQIY7fGzghHnF7bEe7RpTLS4OLVRdJKRrV5cn5xXXRkJaVVygBVSLAWDSThqYzewRSesl7XfOneO3/Y2kD0nql9Qm6dPOuUP+c1+V9BlJQ5K+4Jx7xm+/QdJ3JeVK+pFz7uvT/tsAQJYaGnbqPNg7al6xF5B71BXvS/bLyzFV+tXia84u8zfz8MJxUYhqMQDMhFOpMG+R9ANJj6a0PSvpq865QTN7SNJXJf0XM1sj6U5JayWtkPQrMzvTP+ZhSddJ6pT0OzN7yjm3fXp+DQDIDolqcfLWbP4Uil1dveofGk72KypY4IXi1ZHkvOI6qsUAkBYnDczOuRfNrHpM27+m/PiypI/6j2+W9IRzrk/STjNrlXSJ/1yrc65dkszsCb8vgRnAnDM07LT34DG/Shwfdau28arFtaVhvW91mepSbtFGtRgAMsd0zGHeKOmn/uOV8gJ0QqffJkl7xrRfOg3vDQBpc+T4wAlbP7fHerSzu0f9gyPV4mV+tfh9Z0W8bZ9LQ6orC6uSajEAZIUpBWYz2yRpUFLz9AxHMrNGSY2SVFlZOV0vCwCTkqwWd6Xes9irFseOjlSLc3NMVcUFqo2EdPVZkVG3aCumWgwAWW3SgdnMPiVvMeC1zrnEMu29klaldKvw2xTQPopzrklSkyRFo1E3Xh8AmG5HE9XiMbdoG1stXrp4geoiIV11ZmRkM4+IVy1emEe1GADmokkFZv+OF1+RdJVzrjflqackPWZm35K36K9B0r9LMkkNZlYjLyjfKek/TWXgAHC6hoad3j50bNS84kTVeP+YanFlcYFqS0O66qxIcgpFbWlIxaGFMrM0/hYAgNl2KreVe1zS1ZJKzaxT0oPy7oqxSNKz/v84XnbO/blz7g0z+5m8xXyDku5xzg35r3OvpGfk3VbuEefcGzPw+wCA4n2DJ8wrbovFtbOrR30p1eIl+XmqKwvrvWeOTKGoi4RUWRyiWgwASLKR2RSZJxqNupaWlnQPA0AGGh522nvomNq7evztn0emUuw7MlItzjGpsrggOX3Cuz2b97iEajEAwGdmW51z0fGeY6c/ABkt3jeonWMW201ULa6NhHVF/ZhqcUmBFuXlpvE3AABkOwIzgLQbHnZ6+/CxE+YVt8d69O6R48l+OSat8qvFV9SXJucV10bCKg1TLQYAzAwCM4BZ09M3qJ1dI9s+J0Lxzq64jg+MVIsL8/NUFwnrPfUlI1s/R8KqoloMAEgDAjOAaTU87PTOkePevOLE3Sj8exi/c3h0tbiiqEB1kZDeU1cy6hZtVIsBAJmEwAxgUlKrxalTKNrHVosX5am2LKzLa0tGTaGoKilQ/gKqxQCAzEdgBjChRLV47Lzitlh8VLXYTKooWqy6SFiX1Zaoriyk2tKw6spCioQXUS0GAGQ1AjMA9fYPjqkSe7dq29nVo2MDQ8l+4UV5qouEvFCccos2qsUAgLmMwAzME845vXP4eMpmHiO73b09TrW4ttSrFqfeoi1SSLUYADD/EJiBOSZRLW7vGh2K22MnVotrIyFdWlsysvVzJKTqkhDVYgAAUhCYgSyUqBa3p2zokZhGMbZavHKZN7f4kpriUXeiKKNaDADAKSEwAxnsWP+Qt+XzmA09dnb1qLd/pFocWpirurLUUOwF45pSqsUAAEwVgRlIM+ec3j1yXG37R+5XnJhnvPfQsWS/RLW41q8W1/rziqkWAwAwswjMwCxJVIvH3rO4PXZitbg2EtafVRfpjsiq5DQKqsUAAKQHgRmYRolq8dh7FgdVi6NVxaorC6vOX3hHtRgAgMxCYAYm4Vj/0Im73HXFtTPWo55xqsVRv1qcWHBHtRgAgOxBYAYm4JzTviN9ybtQtAVUi1csXazaSEjRaHFyXnFtJKzyJVSLAQDIdgRmzHuJanF7V3zUwrv2WHxUtbhgYa7q/Grx7aWrkts/15SGtHgh1WIAAOYqAjPmhUS12KsUj64Wv334mJwb6evNLQ7pY9FVo7Z/ploMAMD8RGDGnHJ8YJy5xRNUi2sjIV1cVaTbx8wtploMAABSEZiRdZxz2n+0T23742rzd7cb2eVu4mpxbXJucUhnLMmnWgwAAE4JgRkZK1EtTt3+uS3Wo51dPYr3DSb7pVaLPxapGHXf4oKF/BEHAABTQ5pAWiWrxbGR7Z8T3/ceGr9a/NGLK1Qb8Rbc1ZVRLQYAADOLwIxZcXxgSLu6e7y7UPgL79r96nFqtXjxAq9afFFlkR+Mve2fqRYDAIB0IYFg2qRWi8fudDe2Wrxiab7qysL6yEUrk3ehSMwtzsmhWgwAADIHgRmnLVEtbo+lLLjzw/F41eILU6rFtaUh1UaoFgMAgOxBasG4nHOKHe1Ta/K2bCPbP3cePLFaXBsZqRYn7kZBtRgAAMwFBOZ5LrVanLrgrj3Wo6NjqsU1pSFdsKpIt11YMeoWbVSLAQDAXEbSmQcS1eK22Ojtn9tiJ1aLly/NV10krFsvWpkMxLWRsJZTLQYAAPMUgXkOOT4wpI7u3pG7UKQsvEutFucvyFFtaVjnVywbVS2uKQ0ptIg/EgAAAKlIR1nGOadYvC9ZJU4NxZ0HezU8plpcGwnp1otW+ovtwqoro1oMAABwOgjMGSq1WpzY9rmtq0ft++MnVItrSsM6r2KpbrlwpeqoFgMAAEwrElUaJarFqVXixMK7sdXiM5bkq64slAzFibtRrFi6mGoxAADADCIwz4K+Qa9anHrP4sTdKI4eH6kWL8rLUW0krHOpFgMAAGQMUtg0cc6pK94/plLsBeQ9B06sFtdGQrrlgpWjbs9GtRgAACDzEJhPU6JanJg6kXo3irHV4prSkM5ZuVQ3n79CdWVh1ZaGVRMJKUy1GAAAIGuQ3MaRqBaP2sjDn0oxtlpcvmSR6iJh3XzBCr9S7G3/vHIZ1WIAAIC5gMA8jmv+1wva2dWT/DlZLV7hVYtrI2FvbjHVYgAAgDmPtDeOj1+ySnk5Of40CqrFAAAA8xmBeRyN761L9xAAAACQIXLSPQAAAAAgkxGYAQAAgAAEZgAAACAAgRkAAAAIQGAGAAAAAhCYAQAAgAAEZgAAACAAgRkAAAAIQGAGAAAAAhCYAQAAgAAEZgAAACAAgRkAAAAIcNLAbGaPmNl+M9uW0vYxM3vDzIbNLDqm/1fNrNXM3jKz61Pab/DbWs3s/un9NQAAAICZcSoV5i2SbhjTtk3SbZJeTG00szWS7pS01j/mh2aWa2a5kh6WdKOkNZI+7vcFAAAAMlreyTo45140s+oxbW9KkpmN7X6zpCecc32SdppZq6RL/OdanXPt/nFP+H23T2n0AAAAwAyb7jnMKyXtSfm502+bqP0EZtZoZi1m1hKLxaZ5eAAAAMDpybhFf865Judc1DkXjUQi6R4OAAAA5rmTTsk4TXslrUr5ucJvU0A7AAAAkLGmu8L8lKQ7zWyRmdVIapD075J+J6nBzGrMbKG8hYFPTfN7AwAAANPupBVmM3tc0tWSSs2sU9KDkg5I+r6kiKR/NrPXnXPXO+feMLOfyVvMNyjpHufckP8690p6RlKupEecc2/MxC8EAAAATCdzzqV7DBOKRqOupaUl3cMAAADAHGdmW51z0fGey7hFfwAAAEAmITADAAAAAQjMAAAAQAACMwAAABCAwAwAAAAEIDADAAAAAQjMAAAAQAACMwAAABCAwAwAAAAEIDADAAAAAQjMAAAAQAACMwAAABCAwAwAAAAEIDADAAAAAQjMAAAAQAACMwAAABCAwAwAAAAEIDADAAAAAQjMAAAAQAAC8xjNzc2qrq5WTk6Oqqur1dzcnO4hAQAAII3y0j2ATNLc3KzGxkb19vZKkjo6OtTY2ChJ2rBhQzqHBgAAgDShwpxi06ZNybCc0Nvbq02bNqVpRAAAAEg3AnOK3bt3n1Y7AAAA5j4Cc4rKysrTagcAAMDcR2BOsXnzZhUUFIxqKygo0ObNm9M0IgAAAKQbgTnFhg0b1NTUpKqqKpmZqqqq1NTUxII/AACAecycc+kew4Si0ahraWlJ9zAAAAAwx5nZVudcdLznqDADAAAAAQjMAAAAQAACMwAAABCAwAzgtLB9PABgvmFrbACnjO3jAQDzERVmAKeM7eMBAPMRgRnAKWP7eADAfERgBnDK2D4eADAfEZgBnDK2jwcAzEcEZgCnjO3jAQDzEVtjAwAAYN5ja2wAAABgkgjMAAAAQAACMwAAABCAwAwAAAAEIDADAAAAAQjMAAAAQAACMwAAABCAwAwAAAAEIDADAAAAAQjMAAAAQAACMwAAABCAwAwAAAAEOGlgNrNHzGy/mW1LaSs2s2fNbIf/vchvNzP7npm1mtnvzeyilGPu9vvvMLO7Z+bXAQAAAKbXqVSYt0i6YUzb/ZKec841SHrO/1mSbpTU4H81SvpbyQvYkh6UdKmkSyQ9mAjZAAAAQCY7aWB2zr0o6cCY5psl/cR//BNJt6S0P+o8L0taZmbLJV0v6Vnn3AHn3EFJz+rEEA4AAABknMnOYS53zr3jP35XUrn/eKWkPSn9Ov22idoBAACAjDblRX/OOSfJTcNYJElm1mhmLWbWEovFputlAQAAgEmZbGDe50+1kP99v9++V9KqlH4VfttE7SdwzjU556LOuWgkEpnk8AAAAIDpMdnA/JSkxJ0u7pb0jyntn/TvlnGZpMP+1I1nJH3AzIr8xX4f8NsAAACAjJZ3sg5m9rikqyWVmlmnvLtdfF3Sz8zsM5I6JN3ud39a0k2SWiX1Svq0JDnnDpjZX0v6nd/vr5xzYxcSAgAAABnHvCnImSkajbqWlpZ0DwMAAABznJltdc5Fx3uOnf4AAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAhAYAYAAAACEJgBAACAAARmAAAAIACBGQAAAAgwpcBsZl80s21m9oaZ3ee3FZvZs2a2w/9e5LebmX3PzFrN7PdmdtE0jB8AAACYUZMOzGZ2jqTPSbpE0vmS1ptZvaT7JT3nnGuQ9Jz/syTdKKnB/2qU9LdTGDcAAAAwK6ZSYT5b0ivOuV7n3KCkFyTdJulmST/x+/xE0i3+45slPeo8L0taZmbLp/D+AAAAwIybSmDeJulKMysxswJJN0laJancOfeO3+ddSeX+45WS9qQc3+m3jWJmjWbWYmYtsVhsCsMDAAAApm7Sgdk596akhyT9q6RfSnpd0tCYPk6SO83XbXLORZ1z0UgkMtnhAQAAANNiSov+nHM/ds5d7Jx7r6SDkv4kaV9iqoX/fb/ffa+8CnRChd8GAAAAZKyp3iWjzP9eKW/+8mOSnpJ0t9/lbkn/6D9+StIn/btlXCbpcMrUDQAAACAj5U3x+L83sxJJA5Lucc4dMrOvS/qZmX1GUoek2/2+T8ub59wqqVfSp6f43gAAAMCMm1Jgds5dOU5bt6Rrx2l3ku6ZyvsBAAAAs42d/gAAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAJB2zc3Nqq6uVk5Ojqqrq9Xc3JzuISXlpXsAAAAAmN+am5vV2Nio3t5eSVJHR4caGxslSRs2bEjn0CRRYQYAAECabdq0KRmWE3p7e7Vp06Y0jWg0AjMAAADSavfu3afVPtsIzAAAAEirysrK02qfbQRmAAAApNXmzZtVUFAwqq2goECbN29O04hGIzADAAAgrTZs2KCmpiZVVVXJzFRVVaWmpqaMWPAnSeacS/cYJhSNRl1LS0u6hwEAAIA5zsy2Ouei4z1HhRkAAAAIQGAGAAAAAhCYAQAAgAAEZgAAACAAgRkAAAAIQGAGAAAAAhCYAQAAgAAEZgAAACAAgRkAAAAIQGAGAAAAAkwpMJvZl8zsDTPbZmaPm1m+mdWY2Stm1mpmPzWzhX7fRf7Prf7z1dPyGwAAAAAzaNKB2cxWSvqCpKhz7hxJuZLulPSQpG875+olHZT0Gf+Qz0g66Ld/2+8HAAAAZLSpTsnIk7TYzPIkFUh6R9I1kp70n/+JpFv8xzf7P8t//lozsym+PwAAADCjJh2YnXN7JX1T0m55QfmwpK2SDjnnBv1unZJW+o9XStrjHzvo9y+Z7PsDAAAAs2EqUzKK5FWNayStkBSSdMNUB2RmjWbWYmYtsVhsqi8HAAAATMlUpmS8X9JO51zMOTcg6R8krZO0zJ+iIUkVkvb6j/dKWiVJ/vNLJXWPfVHnXJNzLuqci0YikSkMDwAAAJi6qQTm3ZIuM7MCfy7ytZK2S/qNpI/6fe6W9I/+46f8n+U//2vnnJvC+wMAkDWam5tVXV2tnJwcVVdXq7m5Od1DAnCK8k7eZXzOuVfM7ElJr0oalPSapCZJ/yzpCTP7mt/2Y/+QH0v6v2bWKumAvDtqAAAw5zU3N6uxsVG9vb2SpI6ODjU2NkqSNmzYkM6hATgFlslF3mg06lpaWtI9DAAApqS6ulodHR0ntFdVVWnXrl2zPyAAJzCzrc656HjPsdMfAAAzbPfu3afVDiCzEJgBAJhhlZWVp9UOILMQmAEAmGGbN29WQUHBqLaCggJt3rw5TSMCcDoIzAAAzLANGzaoqalJVVVVMjNVVVWpqamJBX9AlmDRHwAAAOY9Fv0BAAAAk0RgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAIQmAEAAIAABGYAAAAgAIEZAAAACEBgBgAAAAJk9NbYZhaT1JGmty+V1JWm957POO/Zg2s1+zjncwPXMT0479kjXdeqyjkXGe+JjA7M6WRmLRPtJ46Zw3nPHlyr2cc5nxu4junBec8emXitmJIBAAAABCAwAwAAAAEIzBNrSvcA5inOe/bgWs0+zvncwHVMD8579si4a8UcZgAAACAAFWYAAAAgQNYEZjNbZWa/MbPtZvaGmX3Rby82s2fNbIf/vchv32BmvzezP5jZv5nZ+SmvdYOZvWVmrWZ2f8B73u2/7g4zuzul/Q7/td8ws4cCjr/Yf/9WM/uemZnf/jH/2GEzy6hVoKmy9JxvNrM9ZhYf0/4pM4uZ2ev+12encm4yTZqu1S/N7JCZ/WJMe42ZveIf/1MzWzjB8Vn9+ZCy9rzPy89IkAy7jvf6xzozKw04ftzrbWbvNbNXzWzQzD461XMzU7L0nI/bz8yuNrPDKZ+dB6ZybjJNhl2rZv/4bWb2iJktmOD46f98OOey4kvSckkX+Y8LJf1J0hpJ35B0v99+v6SH/MfvkVTkP75R0iv+41xJbZJqJS2U9B+S1ozzfsWS2v3vRf7jIkklknZLivj9fiLp2gnG/O+SLpNkkv5F0o1++9mSzpL0vKRous/tHDvnl/njjo9p/5SkH6T7nM6Va+X3vVbShyT9Ykz7zyTd6T/+35I+P8HxWf35yOLzPi8/I1l0HS+UVC1pl6TSgDGPe739Y8+T9Kikj6b73M6xcz5uP0lXj33NufSVYdfqJnn/zzBJj2vi/85N++cjayrMzrl3nHOv+o+PSnpT0kpJN8sLUPK/3+L3+Tfn3EG//WVJFf7jSyS1OufanXP9kp7wX2Os6yU965w74L/Os5JukHehdzjnYn6/X0n6yNiDzWy5pCXOuZedd5UeTRnbm865tyZ1ImZRtp1zfwwvO+femeSvnLXScK3knHtO0tHUNjMzSddIenLse47pl/WfDyn7zrt//Lz8jATJlOvot7/mnNsVNN6g6+2c2+Wc+72k4cBfOs2y7ZyfTr+5JsOu1dPOJ6/oUjG2z0x9PrImMKcys2p5f9N7RVJ5yn/835VUPs4hn5FXwZK8i7wn5blOv22sifq1SjrLzKrNLE/eRVg1wfGdp/A+WSFLzvnJfMT/Z6InzWwyx2eFWbpWEymRdMg5N3iS4+fU50PKmvN+MvPiMxIkzdfxVE3X9c4IWXLOT+ZyM/sPM/sXM1ubhvefFZlyrfypGJ+Q9Mtxnp6Rz0feVF9gtplZWNLfS7rPOXfE+4uExznnzMyN6f8+eRfsiul4f+fcQTP7vKSfyvsbyr9JqpuO185Uc+Sc/5Okx51zfWb2n+X9jfOa6RhfJkn3tZqv5sh5nxefkSBz5DpmlTlyzl+Vt6Vy3MxukvT/JDWkd0jTL8Ou1Q8lveic++0MvPa4sqrC7P+N4u8lNTvn/sFv3uf/827in3n3p/Q/T9KPJN3snOv2m/dqdHWyQtJeM7s0ZcL+hyfqJ0nOuX9yzl3qnLtc0luS/mRmuSnH/5Xft2K847NJlp3zCTnnup1zff6PP5J08emei0w3y9dqIt2Slvn/EpB6/Jz8fEhZd94nNB8+I0Ey5DoGje8Z//gfaYLrPZnXTacsO+cTcs4dcc7F/cdPS1pgAYsHs1EmXSsze1BSRNKXU9pm/vPhMmBC+al8yZvg/aik74xp/xuNnnT+Df9xpbx/yn/PmP558haT1Whk0vnacd6vWNJOeYvOivzHxf5zZf73IkmvSzpzgjGPXdR005jnn1cGL2rKxnOe8lpjFzQtT3l8q6SX031+s/lapfS/Wicuyvi5Ri+2+IsJjs3qz0e2nveU/vPqM5It1zHluV0KXoAWeL0lbVFmL/rLunM+UT9JZ2hkX4tL5C1St3Sf47l4rSR9Vt6/Mi8+yZin/fOR9gtxGhfsCklO0u/lBabX5a2WLJH0nKQd8haDJQLWjyQdTOnbkvJaN8lb5dkmaVPAe270L3qrpE+ntD8uabv/dWfA8VFJ2/z3+UHKB+pWeXNq+iTtk/RMus/vHDrn3/DP7bD//b/77f9T0hv+B/Q3klan+/zOgWv1W0kxScf8c329314rLwy3yvuP1qK5+PnI4vM+Lz8jWXQdv+D/PCjpbUk/muD4ca+3pD/zj++RV2l7I93ndw6d83H7Sbo35bPzssYExWz/yrBrNegfm3jtB2br88FOfwAAAECArJrDDAAAAMw2AjMAAAAQgMAMAAAABCAwAwAAAAEIzAAAAEAAAjMAAAAQgMAMAAAABCAwAwAAAAH+P0gM5nv+1IMSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = m1.predict(n_periods=N_TEST)\n",
    "preds = pd.DataFrame(data=preds, columns=[\"Preds\"])\n",
    "preds.index = y_test.index\n",
    "\n",
    "print_metrics(y_test.to_numpy(), preds.to_numpy(), None)\n",
    "plot_predictions(y_test, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- SARIMAX (With COVID-flag exogenous variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With SARIMAX we can add the influence of seasonalities and exogenous variables\n",
    "# Since there is a big change of the trend in the last months of the time series\n",
    "# a binary variable (COOVID-19 flag) is addedd to the model\n",
    "\n",
    "covid = pd.DataFrame(\n",
    "    {\n",
    "        \"ds\": df.index.to_numpy(),\n",
    "        \"y\": np.concatenate([np.zeros(len(df) - 8), np.ones(8)]),\n",
    "    }\n",
    ").set_index(\"ds\")\n",
    "\n",
    "exog = prepare_exog(covid.y)\n",
    "exog_train, exog_test = exog[:-N_TEST], exog[-N_TEST:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom GridSearch for SARIMAX\n",
    "\n",
    "max_order = [0, 1] # Due to acf and pacf plots\n",
    "max_seasonal_order = [0, 1, 2]\n",
    "max_diff_order = [0, 1]\n",
    "\n",
    "orders = [(p, d, q) for p in max_order for d in max_diff_order for q in max_order]\n",
    "seasonal_orders = [(p, d, q, 12) for p in max_seasonal_order for d in max_diff_order for q in max_seasonal_order]\n",
    "\n",
    "scores = []\n",
    "\n",
    "for order in orders:\n",
    "    for seasonal_order in seasonal_orders:\n",
    "        m = StatisticalModel(\"SARIMAX\")\n",
    "        res = m.fit_SARIMAX(\n",
    "            y_train,\n",
    "            exog=exog_train,\n",
    "            order=order,\n",
    "            seasonal_order=seasonal_order,\n",
    "        )\n",
    "        preds = res.get_forecast(steps=N_TEST, exog=exog_test).predicted_mean\n",
    "        scores.append([order, seasonal_order, rmse(y_test, preds)])\n",
    "\n",
    "scores = pd.DataFrame(scores, columns=[\"order\", \"seasonal_order\", \"rmse\"])\n",
    "scores.sort_values(by=\"rmse\", inplace=True)\n",
    "scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>44</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)x(2, 1, [1, 2], 12)</td> <th>  Log Likelihood     </th> <td>-190.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                     <td>Sun, 09 Jan 2022</td>           <th>  AIC                </th>  <td>396.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                         <td>21:45:52</td>               <th>  BIC                </th>  <td>408.597</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                      <td>01-01-2017</td>              <th>  HQIC               </th>  <td>400.758</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td>- 08-01-2020</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>const</th>    <td>   -0.8829</td> <td> 1175.746</td> <td>   -0.001</td> <td> 0.999</td> <td>-2305.303</td> <td> 2303.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>y</th>        <td>  208.4435</td> <td>  814.822</td> <td>    0.256</td> <td> 0.798</td> <td>-1388.579</td> <td> 1805.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    0.8528</td> <td>    0.150</td> <td>    5.672</td> <td> 0.000</td> <td>    0.558</td> <td>    1.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>    1.7298</td> <td> 2744.430</td> <td>    0.001</td> <td> 0.999</td> <td>-5377.254</td> <td> 5380.714</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L24</th> <td>   -0.7308</td> <td> 2132.936</td> <td>   -0.000</td> <td> 1.000</td> <td>-4181.208</td> <td> 4179.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -1.8410</td> <td> 2742.890</td> <td>   -0.001</td> <td> 0.999</td> <td>-5377.807</td> <td> 5374.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th> <td>    0.8413</td> <td> 2441.059</td> <td>    0.000</td> <td> 1.000</td> <td>-4783.545</td> <td> 4785.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 6095.6907</td> <td>    1.922</td> <td> 3172.305</td> <td> 0.000</td> <td> 6091.925</td> <td> 6099.457</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>4.28</td> <th>  Jarque-Bera (JB):  </th> <td>94.66</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.04</td> <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>7.86</td> <th>  Skew:              </th> <td>2.32</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th> <td>10.03</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 2.57e+23. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                        SARIMAX Results                                        \n",
       "===============================================================================================\n",
       "Dep. Variable:                                       y   No. Observations:                   44\n",
       "Model:             SARIMAX(1, 0, 0)x(2, 1, [1, 2], 12)   Log Likelihood                -190.436\n",
       "Date:                                 Sun, 09 Jan 2022   AIC                            396.871\n",
       "Time:                                         21:45:52   BIC                            408.597\n",
       "Sample:                                     01-01-2017   HQIC                           400.758\n",
       "                                          - 08-01-2020                                         \n",
       "Covariance Type:                                   opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.8829   1175.746     -0.001      0.999   -2305.303    2303.537\n",
       "y            208.4435    814.822      0.256      0.798   -1388.579    1805.466\n",
       "ar.L1          0.8528      0.150      5.672      0.000       0.558       1.147\n",
       "ar.S.L12       1.7298   2744.430      0.001      0.999   -5377.254    5380.714\n",
       "ar.S.L24      -0.7308   2132.936     -0.000      1.000   -4181.208    4179.747\n",
       "ma.S.L12      -1.8410   2742.890     -0.001      0.999   -5377.807    5374.125\n",
       "ma.S.L24       0.8413   2441.059      0.000      1.000   -4783.545    4785.228\n",
       "sigma2      6095.6907      1.922   3172.305      0.000    6091.925    6099.457\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   4.28   Jarque-Bera (JB):                94.66\n",
       "Prob(Q):                              0.04   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               7.86   Skew:                             2.32\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        10.03\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 2.57e+23. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = StatisticalModel('SARIMAX')\n",
    "res = m2.fit_SARIMAX(y_train, exog=exog_train, order=(1, 0, 0), seasonal_order=(2, 1, 2, 12))\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 88.29\n",
      "MAPE: 14.82%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_train = res.get_prediction().predicted_mean[13:]\n",
    "y_train_ = y_train[13:]\n",
    "print_metrics(y_train_, preds_train, None)\n",
    "plot_predictions(y_train_, preds_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 60.75\n",
      "MAPE: 5.27%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = res.get_forecast(steps=N_TEST, exog=exog_test).predicted_mean\n",
    "print_metrics(y_test, preds, None)\n",
    "plot_predictions(y_test, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Producto B:\n",
    "Montly demand forecast for coffee-based drink sold through channel stores in Colombia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 2)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_data(\"data/producto_B.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_TEST = 2\n",
    "N_FORECAST = 4\n",
    "df.set_index(\"ds\", inplace=True)\n",
    "y_train, y_test = df[\"y\"][:-N_TEST], df[\"y\"][-N_TEST:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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"
    }
   ],
   "source": [
    "# In this time, I recolect the data of confirmed new cases of COVID-19 in Colombia\n",
    "# but it didn't work well because its trend is increasing exponentially\n",
    "# so I decided to use it with another variable, a flag for the first 3 months, \n",
    "# when almost all supply chains were disrupted\n",
    "from deeplearning import scale_data\n",
    "\n",
    "new_cases = get_covid_data(\"Colombia\", start=df.index.min(), end=\"2021-01-01\")\n",
    "\n",
    "# Scale the data to be between 0 and 1 to have a better fit with the model\n",
    "new_cases_scaled, scaler = scale_data([new_cases.new_cases.to_numpy().reshape(-1, 1)], \"minmax\")\n",
    "new_cases.new_cases = new_cases_scaled[0]\n",
    "\n",
    "covid_first_months = pd.DataFrame(\n",
    "    {\n",
    "        \"ds\": pd.date_range(start=df.index.min(), end=\"2021-01-01\", freq=\"MS\"),\n",
    "        \"covid_fm\": np.concatenate(\n",
    "            [np.zeros(len(df) - 6), np.ones(3), np.zeros(7)]\n",
    "        ).astype(int),\n",
    "    }\n",
    ").set_index(\"ds\")\n",
    "\n",
    "exog_vars = pd.concat([covid_first_months, new_cases], axis=1)\n",
    "\n",
    "exog_train = prepare_exog(exog_vars[:-(N_TEST + N_FORECAST)])\n",
    "exog_test = prepare_exog(exog_vars[-(N_TEST + N_FORECAST):-N_FORECAST])\n",
    "exog_forecast = prepare_exog(exog_vars[-N_FORECAST:])\n",
    "exog_vars = prepare_exog(exog_vars[:-N_FORECAST])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- ADF test ---\n",
      "ADF Statistic: -1.252 \n",
      "p-value: 0.651\n"
     ]
    }
   ],
   "source": [
    "print_adf_test(y_train[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_diff = y_train.to_numpy()[:-1] - y_train.to_numpy()[1:]\n",
    "tsplot(y_diff, lags=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>order</th>\n",
       "      <th>seasonal_order</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>(1, 1, 0)</td>\n",
       "      <td>(0, 0, 1, 12)</td>\n",
       "      <td>6234.009361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>(0, 1, 1)</td>\n",
       "      <td>(0, 0, 1, 12)</td>\n",
       "      <td>6277.661669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>(1, 1, 0)</td>\n",
       "      <td>(1, 0, 1, 12)</td>\n",
       "      <td>6362.531439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>(1, 1, 0)</td>\n",
       "      <td>(2, 0, 2, 12)</td>\n",
       "      <td>6385.714869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>(1, 1, 1)</td>\n",
       "      <td>(0, 0, 1, 12)</td>\n",
       "      <td>6390.866697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order seasonal_order         rmse\n",
       "109  (1, 1, 0)  (0, 0, 1, 12)  6234.009361\n",
       "55   (0, 1, 1)  (0, 0, 1, 12)  6277.661669\n",
       "115  (1, 1, 0)  (1, 0, 1, 12)  6362.531439\n",
       "122  (1, 1, 0)  (2, 0, 2, 12)  6385.714869\n",
       "127  (1, 1, 1)  (0, 0, 1, 12)  6390.866697"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Custom GridSearch for SARIMAX\n",
    "\n",
    "max_order = [0, 1] # Due to acf and pacf plots\n",
    "max_seasonal_order = [0, 1, 2]\n",
    "max_diff_order = [0, 1]\n",
    "\n",
    "orders = [(p, d, q) for p in max_order for d in max_diff_order for q in max_order]\n",
    "seasonal_orders = [(p, d, q, 12) for p in max_seasonal_order for d in max_diff_order for q in max_seasonal_order]\n",
    "\n",
    "scores = []\n",
    "\n",
    "for order in orders:\n",
    "    for seasonal_order in seasonal_orders:\n",
    "        m = StatisticalModel(\"SARIMAX\")\n",
    "        res = m.fit_SARIMAX(\n",
    "            y_train,\n",
    "            exog=exog_train,\n",
    "            order=order,\n",
    "            seasonal_order=seasonal_order,\n",
    "        )\n",
    "        preds = res.get_forecast(steps=N_TEST, exog=exog_test).predicted_mean\n",
    "        scores.append([order, seasonal_order, rmse(y_test, preds)])\n",
    "\n",
    "scores = pd.DataFrame(scores, columns=[\"order\", \"seasonal_order\", \"rmse\"])\n",
    "scores.sort_values(by=\"rmse\", inplace=True)\n",
    "scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>31</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 0)x(0, 0, [1], 12)</td> <th>  Log Likelihood     </th> <td>-248.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Mon, 10 Jan 2022</td>         <th>  AIC                </th>  <td>509.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>19:58:49</td>             <th>  BIC                </th>  <td>517.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                     <td>01-01-2018</td>            <th>  HQIC               </th>  <td>511.942</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>- 07-01-2020</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>const</th>     <td>    6.5928</td> <td> 4893.999</td> <td>    0.001</td> <td> 0.999</td> <td>-9585.468</td> <td> 9598.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>covid_fm</th>  <td>-4607.3446</td> <td> 1906.417</td> <td>   -2.417</td> <td> 0.016</td> <td>-8343.852</td> <td> -870.837</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>new_cases</th> <td>-1.481e+04</td> <td> 6657.035</td> <td>   -2.225</td> <td> 0.026</td> <td>-2.79e+04</td> <td>-1762.750</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>   -0.1923</td> <td>    0.203</td> <td>   -0.949</td> <td> 0.343</td> <td>   -0.590</td> <td>    0.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th>  <td>   -0.9991</td> <td>    0.300</td> <td>   -3.327</td> <td> 0.001</td> <td>   -1.588</td> <td>   -0.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 5.642e+05</td> <td>    0.091</td> <td> 6.22e+06</td> <td> 0.000</td> <td> 5.64e+05</td> <td> 5.64e+05</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.08</td> <th>  Jarque-Bera (JB):  </th> <td>1.66</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.78</td> <th>  Prob(JB):          </th> <td>0.44</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.75</td> <th>  Skew:              </th> <td>-0.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.13</td> <th>  Kurtosis:          </th> <td>3.15</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+25. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                      SARIMAX Results                                       \n",
       "============================================================================================\n",
       "Dep. Variable:                                    y   No. Observations:                   31\n",
       "Model:             SARIMAX(1, 1, 0)x(0, 0, [1], 12)   Log Likelihood                -248.626\n",
       "Date:                              Mon, 10 Jan 2022   AIC                            509.252\n",
       "Time:                                      19:58:49   BIC                            517.659\n",
       "Sample:                                  01-01-2018   HQIC                           511.942\n",
       "                                       - 07-01-2020                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          6.5928   4893.999      0.001      0.999   -9585.468    9598.654\n",
       "covid_fm   -4607.3446   1906.417     -2.417      0.016   -8343.852    -870.837\n",
       "new_cases  -1.481e+04   6657.035     -2.225      0.026   -2.79e+04   -1762.750\n",
       "ar.L1         -0.1923      0.203     -0.949      0.343      -0.590       0.205\n",
       "ma.S.L12      -0.9991      0.300     -3.327      0.001      -1.588      -0.410\n",
       "sigma2      5.642e+05      0.091   6.22e+06      0.000    5.64e+05    5.64e+05\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.08   Jarque-Bera (JB):                 1.66\n",
       "Prob(Q):                              0.78   Prob(JB):                         0.44\n",
       "Heteroskedasticity (H):               2.75   Skew:                            -0.57\n",
       "Prob(H) (two-sided):                  0.13   Kurtosis:                         3.15\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+25. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = StatisticalModel('SARIMAX')\n",
    "res = m2.fit_SARIMAX(y_train, exog=exog_train, order=(1, 1, 0), seasonal_order=(0, 0, 1, 12))\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1015.71\n",
      "MAPE: 27.37%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds_train = res.get_prediction().predicted_mean[13:]\n",
    "y_train_ = y_train[13:]\n",
    "print_metrics(y_train_, preds_train, None)\n",
    "plot_predictions(y_train_, preds_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 6234.01\n",
      "MAPE: 209.84%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see that the model has a moderate fit on the training data,\n",
    "# but the model is not able to capture the real influence of the new cases variable,\n",
    "# so when new cases growth significantly, the model will be affected and the forecaste will be biased \n",
    "# however, the model is able to capture the trend of the data but with negative values (-6000 aprox in each point vs actuals)\n",
    "\n",
    "preds = res.get_forecast(steps=N_TEST, exog=exog_test).predicted_mean\n",
    "print_metrics(y_test, preds, None)\n",
    "plot_predictions(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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"
    }
   ],
   "source": [
    "# We try to use this model and forecast the next 4 months with the +6000 biased forecast\n",
    "# but it doesn't wotk as we see in the plot.\n",
    "# If we analyze visually the data, we can se a big dropout in the first months of the COVID-19\n",
    "# but its trend is recovering faster as well, so I suggest to just use a simple regression model\n",
    "# takeing the first months of data until the dropout and use it as a base for the model\n",
    "\n",
    "preds_train = res.get_prediction().predicted_mean[1:]\n",
    "exog_forecast = pd.concat([exog_test, exog_forecast], axis=0)\n",
    "preds = res.get_forecast(steps=N_TEST + N_FORECAST, exog=exog_forecast).predicted_mean\n",
    "preds = preds_train.append(preds)\n",
    "preds[-(N_TEST + N_FORECAST):] = preds[-(N_TEST + N_FORECAST):] + 6000\n",
    "plot_predictions(df.y, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Producto C:\n",
    "Daily demand forecast for carbonated beverage sold through retail channels in Ecuador."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(640, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_data(\"data/producto_C.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Learning Model (CNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first approach to model the daily demand is to use a deep learning model to capture the non-linearities of the data as well of the influence of the past values of the series. \n",
    "I tried well-known architectures such as LSTM and GRU, but the 1-Dimensional Convolution NN outperformed in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, split the data into train, validation and test sets\n",
    "# Then, prepare the data for the model, it is important to scale the data\n",
    "# Finally, transform the data into vectors to be used by the model\n",
    "\n",
    "train, val, test = time_series_split(df, n_val=60, n_test=30)\n",
    "data_scaled, scaler = scale_data([train, val, test], scaler_type=\"standard\")\n",
    "train_set, val_set, test_set = time_series_to_X_y(data_scaled, window_size=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 6, 200)            600       \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1200)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 20)                24020     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 21        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 24,641\n",
      "Trainable params: 24,641\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-09 21:48:27.176863: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# The architecture is based of a CNN layer of 200 filters,\n",
    "# each input vector have a size of 7 (7 days to predict the next day)\n",
    "# alse dense layers were added to output the final result\n",
    "\n",
    "m1 = DeepLearningModel(\"CNN\", filters=200, window_size=7)\n",
    "m1.model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "17/17 [==============================] - 1s 10ms/step - loss: 1.0220 - root_mean_squared_error: 1.0109 - val_loss: 0.5162 - val_root_mean_squared_error: 0.7185\n",
      "Epoch 2/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.8657 - root_mean_squared_error: 0.9304 - val_loss: 0.4369 - val_root_mean_squared_error: 0.6610\n",
      "Epoch 3/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.7549 - root_mean_squared_error: 0.8688 - val_loss: 0.3845 - val_root_mean_squared_error: 0.6201\n",
      "Epoch 4/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.6924 - root_mean_squared_error: 0.8321 - val_loss: 0.3480 - val_root_mean_squared_error: 0.5899\n",
      "Epoch 5/50\n",
      "17/17 [==============================] - 0s 3ms/step - loss: 0.6401 - root_mean_squared_error: 0.8001 - val_loss: 0.3276 - val_root_mean_squared_error: 0.5724\n",
      "Epoch 6/50\n",
      "17/17 [==============================] - 0s 3ms/step - loss: 0.6026 - root_mean_squared_error: 0.7763 - val_loss: 0.3118 - val_root_mean_squared_error: 0.5584\n",
      "Epoch 7/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.5761 - root_mean_squared_error: 0.7590 - val_loss: 0.2985 - val_root_mean_squared_error: 0.5464\n",
      "Epoch 8/50\n",
      "17/17 [==============================] - 0s 3ms/step - loss: 0.5538 - root_mean_squared_error: 0.7442 - val_loss: 0.2840 - val_root_mean_squared_error: 0.5329\n",
      "Epoch 9/50\n",
      "17/17 [==============================] - 0s 3ms/step - loss: 0.5376 - root_mean_squared_error: 0.7332 - val_loss: 0.2763 - val_root_mean_squared_error: 0.5256\n",
      "Epoch 10/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.5228 - root_mean_squared_error: 0.7230 - val_loss: 0.2737 - val_root_mean_squared_error: 0.5232\n",
      "Epoch 11/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.5101 - root_mean_squared_error: 0.7142 - val_loss: 0.2660 - val_root_mean_squared_error: 0.5158\n",
      "Epoch 12/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.5001 - root_mean_squared_error: 0.7072 - val_loss: 0.2633 - val_root_mean_squared_error: 0.5131\n",
      "Epoch 13/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4926 - root_mean_squared_error: 0.7018 - val_loss: 0.2591 - val_root_mean_squared_error: 0.5090\n",
      "Epoch 14/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4850 - root_mean_squared_error: 0.6964 - val_loss: 0.2581 - val_root_mean_squared_error: 0.5081\n",
      "Epoch 15/50\n",
      "17/17 [==============================] - 0s 3ms/step - loss: 0.4772 - root_mean_squared_error: 0.6908 - val_loss: 0.2574 - val_root_mean_squared_error: 0.5073\n",
      "Epoch 16/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4717 - root_mean_squared_error: 0.6868 - val_loss: 0.2530 - val_root_mean_squared_error: 0.5030\n",
      "Epoch 17/50\n",
      "17/17 [==============================] - 0s 3ms/step - loss: 0.4697 - root_mean_squared_error: 0.6853 - val_loss: 0.2528 - val_root_mean_squared_error: 0.5028\n",
      "Epoch 18/50\n",
      "17/17 [==============================] - 0s 3ms/step - loss: 0.4625 - root_mean_squared_error: 0.6801 - val_loss: 0.2547 - val_root_mean_squared_error: 0.5047\n",
      "Epoch 19/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4600 - root_mean_squared_error: 0.6782 - val_loss: 0.2523 - val_root_mean_squared_error: 0.5023\n",
      "Epoch 20/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4544 - root_mean_squared_error: 0.6741 - val_loss: 0.2516 - val_root_mean_squared_error: 0.5016\n",
      "Epoch 21/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4481 - root_mean_squared_error: 0.6694 - val_loss: 0.2516 - val_root_mean_squared_error: 0.5016\n",
      "Epoch 22/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4483 - root_mean_squared_error: 0.6696 - val_loss: 0.2528 - val_root_mean_squared_error: 0.5028\n",
      "Epoch 23/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4440 - root_mean_squared_error: 0.6664 - val_loss: 0.2541 - val_root_mean_squared_error: 0.5041\n",
      "Epoch 24/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4412 - root_mean_squared_error: 0.6642 - val_loss: 0.2540 - val_root_mean_squared_error: 0.5040\n",
      "Epoch 25/50\n",
      "17/17 [==============================] - 0s 2ms/step - loss: 0.4364 - root_mean_squared_error: 0.6606 - val_loss: 0.2546 - val_root_mean_squared_error: 0.5046\n"
     ]
    }
   ],
   "source": [
    "# Here the validation set help us to overfit during training \n",
    "# by using early stopping method (stop the training when the validation loss doesn't improve)\n",
    "\n",
    "m1.fit(train_set, val_set, epochs=50, early_stop=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m1.plot_loss_function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 175.42\n",
      "MAPE: 39.49%\n",
      "MASE: 149.70%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test = test_set[0]\n",
    "y_preds = m1.predict(X_test) # Due to transform for NN model the test set loss the last 7 preds\n",
    "y_preds = scaler.inverse_transform(y_preds).flatten()\n",
    "\n",
    "y_train = np.concatenate([train,val]).flatten()\n",
    "y_test = test.flatten()[:len(y_preds)] # Ensure the test set is the same length as the preds\n",
    "\n",
    "print_metrics(y_test, y_preds, y_train)\n",
    "plot_predictions(y_test, y_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auto ML Model (Prophet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prophet is a library created by Facebook (now Meta) based on an additive model where \"non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effectts\" (See https://facebook.github.io/prophet/)\n",
    "\n",
    "It is well known in the industry for the precision, speed and scalability of its models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = df[:-30]\n",
    "test_set = df[-30:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# There are evident peaks in the data, so we inpu them into the model as a variable \"holidays\"\n",
    "\n",
    "holidays = pd.DataFrame(\n",
    "    {\n",
    "        \"holiday\": [\"Christmas\", \"Christmas\", \"New Year\", \"New Year\"],\n",
    "        \"ds\": [\"2016-12-24\", \"2017-12-24\", \"2016-12-31\", \"2017-12-31\"],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial log joint probability = -3.88756\n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "      99        1679.4    0.00103033        83.945           1           1      133   \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     199       1680.86   0.000625385       249.236   5.983e-06       0.001      294  LS failed, Hessian reset \n",
      "    Iter      log prob        ||dx||      ||grad||       alpha      alpha0  # evals  Notes \n",
      "     236       1680.95   2.68433e-05       66.2475   3.752e-07       0.001      383  LS failed, Hessian reset \n",
      "     260       1680.95    4.1947e-08       62.4923      0.3166      0.3166      418   \n",
      "Optimization terminated normally: \n",
      "  Convergence detected: relative gradient magnitude is below tolerance\n"
     ]
    }
   ],
   "source": [
    "m2 = AutoMLModel(\"Prophet\", yearly_seasonality=True, weekly_seasonality=True, holidays=holidays)\n",
    "m2.fit(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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            "type": "surface"
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          "table": [
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            "header": {
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             "line": {
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            "type": "table"
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         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
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           "arrowwidth": 1
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          "autotypenumbers": "strict",
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          "colorway": [
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           "#B6E880",
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          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
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           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
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            "linecolor": "white",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "width": 900,
        "xaxis": {
         "rangeselector": {
          "buttons": [
           {
            "count": 7,
            "label": "1w",
            "step": "day",
            "stepmode": "backward"
           },
           {
            "count": 1,
            "label": "1m",
            "step": "month",
            "stepmode": "backward"
           },
           {
            "count": 6,
            "label": "6m",
            "step": "month",
            "stepmode": "backward"
           },
           {
            "count": 1,
            "label": "1y",
            "step": "year",
            "stepmode": "backward"
           },
           {
            "step": "all"
           }
          ]
         },
         "rangeslider": {
          "visible": true
         },
         "title": {
          "text": "ds"
         },
         "type": "date"
        },
        "yaxis": {
         "title": {
          "text": "y"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast = m2.predict(periods=30, freq=\"D\")\n",
    "m2.plot_model_forecast(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 144.57\n",
      "MAPE: 33.67%\n",
      "MASE: 127.15%\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test = test_set.y.to_numpy()\n",
    "y_preds = forecast.tail(len(test_set)).yhat.to_numpy()\n",
    "y_train = train_set.y.to_numpy()\n",
    "\n",
    "print_metrics(y_test, y_preds, y_train)\n",
    "plot_prophet_predictions(test_set, forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 78.98\n",
      "MAPE: 31.86%\n",
      "MASE: 89.29%\n"
     ]
    }
   ],
   "source": [
    "# Plot metrics comparable to the DeepLearning Model (23 steps)\n",
    "print_metrics(y_test[:-7], y_preds[:-7], y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we compare our first CNN approach with the Prophet model, we can see that\n",
    "over simple CNN model (without holidays data) is close to the last one.\n",
    "However, both models can be improved.\n",
    "Here are some ideas to do it:\n",
    "1. Use a more complex model (LSTM, GRU, RNN, or a mix of them)\n",
    "2. Use a cross validation method to avoid overfitting\n",
    "3. Add more features to the NN model, like holidays and seasonalities (eg. cos(day of the week))"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a338e009ce7ffa4d2f6d7716c53ce63ec64b9e801f10b7decac7ebd435fb7c22"
  },
  "kernelspec": {
   "display_name": "Python 3.9.9 64-bit ('venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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