1.01 - Time Series Forecasting - Statistical Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d070c15",
   "metadata": {},
   "source": [
    "# Introduction to Time Series with Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eedda1a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2ad041a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    y\n",
       "0    1949-01-01  112\n",
       "1    1949-02-01  118\n",
       "2    1949-03-01  132\n",
       "3    1949-04-01  129\n",
       "4    1949-05-01  121\n",
       "..          ...  ...\n",
       "139  1960-08-01  606\n",
       "140  1960-09-01  508\n",
       "141  1960-10-01  461\n",
       "142  1960-11-01  390\n",
       "143  1960-12-01  432\n",
       "\n",
       "[144 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"air_passengers.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "156831db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date    object\n",
       "y        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9100c3f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df.date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4708f6e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date    datetime64[ns]\n",
       "y                int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "263eecf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='date', y='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a1449918",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>y</th>\n",
       "      <th>y_log</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "      <td>4.718499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "      <td>4.770685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "      <td>4.882802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "      <td>4.859812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "      <td>4.795791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>606</td>\n",
       "      <td>6.406880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>508</td>\n",
       "      <td>6.230481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>461</td>\n",
       "      <td>6.133398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>390</td>\n",
       "      <td>5.966147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>432</td>\n",
       "      <td>6.068426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date    y     y_log\n",
       "0   1949-01-01  112  4.718499\n",
       "1   1949-02-01  118  4.770685\n",
       "2   1949-03-01  132  4.882802\n",
       "3   1949-04-01  129  4.859812\n",
       "4   1949-05-01  121  4.795791\n",
       "..         ...  ...       ...\n",
       "139 1960-08-01  606  6.406880\n",
       "140 1960-09-01  508  6.230481\n",
       "141 1960-10-01  461  6.133398\n",
       "142 1960-11-01  390  5.966147\n",
       "143 1960-12-01  432  6.068426\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['y_log'] = np.log(df.y)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30148b44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='date', y='y_log')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fc06810",
   "metadata": {},
   "source": [
    "# Fit a Models and make predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1a828ab1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>y</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>606</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>508</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>461</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>390</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date    y unique_id\n",
       "0   1949-01-01  112         1\n",
       "1   1949-02-01  118         1\n",
       "2   1949-03-01  132         1\n",
       "3   1949-04-01  129         1\n",
       "4   1949-05-01  121         1\n",
       "..         ...  ...       ...\n",
       "139 1960-08-01  606         1\n",
       "140 1960-09-01  508         1\n",
       "141 1960-10-01  461         1\n",
       "142 1960-11-01  390         1\n",
       "143 1960-12-01  432         1\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['date', 'y']]\n",
    "df['unique_id'] = \"1\"\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b81bf7d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01-01</td>\n",
       "      <td>112</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02-01</td>\n",
       "      <td>118</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03-01</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04-01</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05-01</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>606</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>508</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>461</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>390</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds    y unique_id\n",
       "0   1949-01-01  112         1\n",
       "1   1949-02-01  118         1\n",
       "2   1949-03-01  132         1\n",
       "3   1949-04-01  129         1\n",
       "4   1949-05-01  121         1\n",
       "..         ...  ...       ...\n",
       "139 1960-08-01  606         1\n",
       "140 1960-09-01  508         1\n",
       "141 1960-10-01  461         1\n",
       "142 1960-11-01  390         1\n",
       "143 1960-12-01  432         1\n",
       "\n",
       "[144 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['ds', 'y', 'unique_id']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "402e41f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/marbel/anaconda3/envs/ts/lib/python3.8/site-packages/statsforecast/core.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from tqdm.autonotebook import tqdm\n"
     ]
    }
   ],
   "source": [
    "from statsforecast import StatsForecast\n",
    "from statsforecast.models import AutoARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "79337015",
   "metadata": {},
   "outputs": [],
   "source": [
    "sf = StatsForecast(\n",
    "    models = [AutoARIMA(season_length = 12)],\n",
    "    freq = 'M'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "078adef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StatsForecast(models=[AutoARIMA])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "edee8452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>AutoARIMA</th>\n",
       "      <th>AutoARIMA-lo-95</th>\n",
       "      <th>AutoARIMA-hi-95</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>444.300049</td>\n",
       "      <td>421.179474</td>\n",
       "      <td>467.420624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-02-01</td>\n",
       "      <td>418.210022</td>\n",
       "      <td>389.988434</td>\n",
       "      <td>446.431610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-03-01</td>\n",
       "      <td>446.237030</td>\n",
       "      <td>412.620667</td>\n",
       "      <td>479.853394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-04-01</td>\n",
       "      <td>488.228943</td>\n",
       "      <td>450.265289</td>\n",
       "      <td>526.192566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-05-01</td>\n",
       "      <td>499.231354</td>\n",
       "      <td>457.290192</td>\n",
       "      <td>541.172546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-06-01</td>\n",
       "      <td>562.230652</td>\n",
       "      <td>516.679749</td>\n",
       "      <td>607.781555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-07-01</td>\n",
       "      <td>649.230835</td>\n",
       "      <td>600.329895</td>\n",
       "      <td>698.131836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-08-01</td>\n",
       "      <td>633.230774</td>\n",
       "      <td>581.196655</td>\n",
       "      <td>685.264893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-09-01</td>\n",
       "      <td>535.230774</td>\n",
       "      <td>480.241302</td>\n",
       "      <td>590.220276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-10-01</td>\n",
       "      <td>488.230804</td>\n",
       "      <td>430.436981</td>\n",
       "      <td>546.024597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-11-01</td>\n",
       "      <td>417.230804</td>\n",
       "      <td>356.762543</td>\n",
       "      <td>477.699036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-12-01</td>\n",
       "      <td>459.230804</td>\n",
       "      <td>396.201508</td>\n",
       "      <td>522.260071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ds   AutoARIMA  AutoARIMA-lo-95  AutoARIMA-hi-95\n",
       "unique_id                                                         \n",
       "1         1961-01-01  444.300049       421.179474       467.420624\n",
       "1         1961-02-01  418.210022       389.988434       446.431610\n",
       "1         1961-03-01  446.237030       412.620667       479.853394\n",
       "1         1961-04-01  488.228943       450.265289       526.192566\n",
       "1         1961-05-01  499.231354       457.290192       541.172546\n",
       "1         1961-06-01  562.230652       516.679749       607.781555\n",
       "1         1961-07-01  649.230835       600.329895       698.131836\n",
       "1         1961-08-01  633.230774       581.196655       685.264893\n",
       "1         1961-09-01  535.230774       480.241302       590.220276\n",
       "1         1961-10-01  488.230804       430.436981       546.024597\n",
       "1         1961-11-01  417.230804       356.762543       477.699036\n",
       "1         1961-12-01  459.230804       396.201508       522.260071"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import timedelta\n",
    "\n",
    "forecast_df = sf.predict(h=12, level=[95]) \n",
    "forecast_df['ds'] = forecast_df['ds'] + timedelta(days=1)\n",
    "forecast_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7609805a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>unique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>606</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>508</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>461</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>390</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds    y unique_id\n",
       "139 1960-08-01  606         1\n",
       "140 1960-09-01  508         1\n",
       "141 1960-10-01  461         1\n",
       "142 1960-11-01  390         1\n",
       "143 1960-12-01  432         1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac3bf10",
   "metadata": {},
   "source": [
    "# Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c315aaa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132, 3)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = df[df.ds <= '1959-12-31']\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a9ee20b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = df[df.ds > '1959-12-31'] \n",
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6b2c214f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display, Markdown\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from statsforecast import StatsForecast\n",
    "from statsforecast.utils import AirPassengersDF\n",
    "from statsforecast import StatsForecast\n",
    "from statsforecast.models import AutoARIMA, AutoETS, Naive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "85b85723",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monthly data\n",
    "season_length = 12 \n",
    "\n",
    "# Period we are predicting forward\n",
    "horizon = len(y_test) # 12 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad7d5544",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [\n",
    "    AutoARIMA(season_length=season_length),\n",
    "    AutoETS(season_length=season_length),\n",
    "    Naive()\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "26d1c277",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instansiate the StatsForecast class as sf\n",
    "sf = StatsForecast(\n",
    "    df=y_train,\n",
    "    models=models,\n",
    "    freq='M', \n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3d0c1351",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forecast for the defined horizon\n",
    "y_preds = sf.forecast(horizon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "54737afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>AutoARIMA</th>\n",
       "      <th>AutoETS</th>\n",
       "      <th>Naive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_id</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>424.160156</td>\n",
       "      <td>406.651276</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-02-01</td>\n",
       "      <td>407.081696</td>\n",
       "      <td>401.732910</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-03-01</td>\n",
       "      <td>470.860535</td>\n",
       "      <td>456.289642</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-04-01</td>\n",
       "      <td>460.913605</td>\n",
       "      <td>440.870514</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-05-01</td>\n",
       "      <td>484.900879</td>\n",
       "      <td>440.333923</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-06-01</td>\n",
       "      <td>536.903931</td>\n",
       "      <td>496.866058</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-07-01</td>\n",
       "      <td>612.903198</td>\n",
       "      <td>545.839111</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>623.903381</td>\n",
       "      <td>544.672485</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>527.903320</td>\n",
       "      <td>477.034485</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>471.903320</td>\n",
       "      <td>412.423096</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>426.903320</td>\n",
       "      <td>357.949158</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>469.903320</td>\n",
       "      <td>402.032745</td>\n",
       "      <td>405.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ds   AutoARIMA     AutoETS  Naive\n",
       "unique_id                                          \n",
       "1         1960-01-01  424.160156  406.651276  405.0\n",
       "1         1960-02-01  407.081696  401.732910  405.0\n",
       "1         1960-03-01  470.860535  456.289642  405.0\n",
       "1         1960-04-01  460.913605  440.870514  405.0\n",
       "1         1960-05-01  484.900879  440.333923  405.0\n",
       "1         1960-06-01  536.903931  496.866058  405.0\n",
       "1         1960-07-01  612.903198  545.839111  405.0\n",
       "1         1960-08-01  623.903381  544.672485  405.0\n",
       "1         1960-09-01  527.903320  477.034485  405.0\n",
       "1         1960-10-01  471.903320  412.423096  405.0\n",
       "1         1960-11-01  426.903320  357.949158  405.0\n",
       "1         1960-12-01  469.903320  402.032745  405.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds['ds'] = y_preds['ds'] + timedelta(days=1)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3ccd605e",
   "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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>AutoARIMA</th>\n",
       "      <th>AutoETS</th>\n",
       "      <th>Naive</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>424.160156</td>\n",
       "      <td>406.651276</td>\n",
       "      <td>405.0</td>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-02-01</td>\n",
       "      <td>407.081696</td>\n",
       "      <td>401.732910</td>\n",
       "      <td>405.0</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-03-01</td>\n",
       "      <td>470.860535</td>\n",
       "      <td>456.289642</td>\n",
       "      <td>405.0</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-04-01</td>\n",
       "      <td>460.913605</td>\n",
       "      <td>440.870514</td>\n",
       "      <td>405.0</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-05-01</td>\n",
       "      <td>484.900879</td>\n",
       "      <td>440.333923</td>\n",
       "      <td>405.0</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-06-01</td>\n",
       "      <td>536.903931</td>\n",
       "      <td>496.866058</td>\n",
       "      <td>405.0</td>\n",
       "      <td>535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-07-01</td>\n",
       "      <td>612.903198</td>\n",
       "      <td>545.839111</td>\n",
       "      <td>405.0</td>\n",
       "      <td>622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-08-01</td>\n",
       "      <td>623.903381</td>\n",
       "      <td>544.672485</td>\n",
       "      <td>405.0</td>\n",
       "      <td>606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-09-01</td>\n",
       "      <td>527.903320</td>\n",
       "      <td>477.034485</td>\n",
       "      <td>405.0</td>\n",
       "      <td>508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-10-01</td>\n",
       "      <td>471.903320</td>\n",
       "      <td>412.423096</td>\n",
       "      <td>405.0</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-11-01</td>\n",
       "      <td>426.903320</td>\n",
       "      <td>357.949158</td>\n",
       "      <td>405.0</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1960-12-01</td>\n",
       "      <td>469.903320</td>\n",
       "      <td>402.032745</td>\n",
       "      <td>405.0</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds   AutoARIMA     AutoETS  Naive    y\n",
       "0          1 1960-01-01  424.160156  406.651276  405.0  417\n",
       "1          1 1960-02-01  407.081696  401.732910  405.0  391\n",
       "2          1 1960-03-01  470.860535  456.289642  405.0  419\n",
       "3          1 1960-04-01  460.913605  440.870514  405.0  461\n",
       "4          1 1960-05-01  484.900879  440.333923  405.0  472\n",
       "5          1 1960-06-01  536.903931  496.866058  405.0  535\n",
       "6          1 1960-07-01  612.903198  545.839111  405.0  622\n",
       "7          1 1960-08-01  623.903381  544.672485  405.0  606\n",
       "8          1 1960-09-01  527.903320  477.034485  405.0  508\n",
       "9          1 1960-10-01  471.903320  412.423096  405.0  461\n",
       "10         1 1960-11-01  426.903320  357.949158  405.0  390\n",
       "11         1 1960-12-01  469.903320  402.032745  405.0  432"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds = y_preds.merge(y_test, how='inner', on=['unique_id', 'ds'])\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6a621949",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df = pd.concat([y_train.tail(24), y_preds]).set_index('ds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "42287455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8eNB4FT1X59///neZ3+/fv79cXV1LHIuKijLeP/XUU6WuUXRuMzMzdenSpVLnNWvWzOJWcL83fPhwSYUNtPJuxwYAAADg9vHMGQAAAAAV1qBBA5lMplLHfXx8lJ2drYYNG5Y6x9fX13ifk5NjMZacnGy8b9OmTYVz3Xq1TPv27VW/fn2dOHFC48eP1yeffKLHH39cHTp0UKtWreTm5lbhdSti7dq1mjt3rr7//vtix3Or8+fPl7lORc/Z7ZzbWz/f6qGHHiozy8MPP2y8P3jwYJlzAQAAANw+mjMAAAAAKqyk25TdysnJqdx5RXMk6caNGxZjGRkZd5TrypUrxntXV1etWbNGf/rTn3T48GElJSUpKSlJkuTh4aEOHTpo8ODBeuqpp+Ts7HxH+5MKr5QZNWqUPvzwwwrNv3r1apnjFT1nd3pubxUQEFBmlsDAQOP9xYsXy5wLAAAA4PbRnAEAAABQadzaUEhMTJSfn1+Fvvf7ZkPTpk2VkpKiNWvWaM2aNdq6dauOHz+uq1evKiEhQQkJCXr33Xe1bt26chsVpVm0aJHRmGnevLnGjx+v1q1bq06dOvL09DQaP88++6w++ugjmc3mO9qPNZR19RMAAAAA66M5AwAAAKDSuLUZ4+bmpsjIyDtey9nZWf369VO/fv0kSWlpaVq/fr0++OAD7dmzR3v27NHzzz+vVatW3dH6CxculCQ9+OCD2r59uzw8PEqcl5mZeUfrW9O5c+cqPF6rVi1rxwEAAADuO07lTwEAAAAA24iJiTHeb9iw4Z6uHRwcrOHDh2vHjh1q0aKFJOnbb78tdruxil5VcujQIUlS3759S23MmM1m7d279y5SW0fRbd4qMn43DbKK4CoeAAAA3I9ozgAAAACoNNq3b29cqTFv3jxlZ2ff8324urqqQ4cOkqTr16/r0qVLFuPu7u6SpLy8vDLXuX79uiTL59383jfffKOzZ8/eRVrrSElJ0b59+0odX7RokaTCq49iY2OtmqXofEvln3MAAACgqqA5AwAAAKDScHd316RJkyRJ6enpGjhwoHJzc0udn5OTozlz5lhs++GHH3Ts2LFSv5Ofn6+tW7dKkry8vOTv728xHhwcLEk6fvx4mVkbNGggSVqzZk2Jty47fvy4xo4dW+Ya9vTcc8+VeG4//fRTrVu3TpLUr18/43xYy63rl3fOAQAAgKqCZ84AAAAAqFSmTJmizZs3a/PmzVq/fr2aNm2q0aNHq02bNvL19VVOTo5++uknJSYmavXq1XJ3d9e4ceOM72/evFmvv/66Hn30UfXq1UtRUVHy9/fX1atXdfToUc2bN8+41djIkSPl4mL5v0Vt27bVli1blJSUpJkzZ6pHjx6qXr26JMnDw0N16tSRJD377LOaPHmyfv31V7Vt21ZTpkxRRESErl27pu+++06zZ89WXl6eWrRoUelubdaqVSslJyerVatWevnll9WsWTNlZWVpxYoVmj9/viSpRo0a+sc//mH1LG3btjXev/TSS3rttdcUHBxs3O4sLCys2D8jAAAAwNFR4QIAAACoVJydnbVmzRqNHj1ay5Yt0+nTp/Xqq6+WOj8gIKDYtps3b2rr1q3GFTIl6d+/v958881i28eMGaO5c+fq4sWLmjp1qqZOnWqMdejQQYmJiZKkF198URs3btSGDRt05MgRDR8+3GIdDw8PLVu2TGvXrq10zZlevXqpV69emjFjhoYNG1Zs3NvbW998843CwsKsniU8PFwDBgzQF198oQ0bNhR71lBqaqpNcgAAAAC2xG3NAAAAAFQ6Hh4eWrp0qZKTkzVmzBhFRETIx8dHLi4u8vX1VfPmzTVixAitWLFChw8ftvjulClTtG7dOr300kt65JFH9MADD8jd3V3u7u4KCwvTU089pbVr12rlypUWzzspUqdOHe3evVsjRoxQeHh4iXOkwmfXrF27Vu+//75atWolT09PeXh4KDw8XKNHj9bevXv15JNPWuX83Avx8fFKSEhQr169FBgYKDc3N4WFhWns2LE6dOiQ8VweW/j444/11ltv6eGHH5aPj4+cnPhfVQAAAFRtJrPZbLZ3CAAAAACA9RXdKmz69OmKj4+32n6GDh2qpUuXKjQ0VCdPnrTKPhITE9WxY0dJ0pYtWxQbG2uV/QAAAADWwG3NAAAAAABWUVBQoIMHDxqfGzVqJFdX1zteLyMjQxkZGZIKb3cGAAAAOCqaMwAAAAAAqzh79qyaNWtmfL7b58d88MEHmjFjxj1IBgAAANgXN/IFAAAAAAAAAACwIZ45AwAAAAD3CVs9cwYAAABA2bhyBgAAAAAAAAAAwIZ45gwAAAAA3Ce4cQIAAABQOXDlDAAAAAAAAAAAgA3RnAEAAAAAAAAAALAhmjMAAAAAAAAAAAA2RHMGAAAAAAAAAADAhmjOAAAAAAAAAAAA2BDNGQAAAAAAAAAAABuiOQMAAAAAAAAAAGBDNGcAAAAAAAAAAABs6P8DA+aEyDYv+hgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize = (20, 7))\n",
    "\n",
    "plot_df[['y', 'AutoARIMA', 'AutoETS']].plot(ax=ax, linewidth=2)\n",
    "\n",
    "ax.set_title('Air Passengers Forecast', fontsize=22)\n",
    "ax.set_ylabel('Monthly Passengers', fontsize=20)\n",
    "ax.set_xlabel('Timestamp [t]', fontsize=20)\n",
    "ax.legend(prop={'size': 15})\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dbb77fe",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7c3ace1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def MAE(true, preds):\n",
    "    return np.mean( np.abs( true - preds ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bbaefa9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.550587972005207"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE(y_preds['y'], y_preds['AutoARIMA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4d45d914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.612475077311196"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE(y_preds['y'], y_preds['AutoETS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "16d7d389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE(y_preds['y'], y_preds['Naive'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5f62b347",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds['ensamble'] = (y_preds['AutoARIMA'] + y_preds['AutoETS']) / 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8868af32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.023768107096355"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAE(y_preds['y'], y_preds['ensamble'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "437ab686",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac15154b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7eea484",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab88c605",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6104fa95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46808f0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae19951b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05ea582c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e674442c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18f7f4ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:ts]",
   "language": "python",
   "name": "conda-env-ts-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}