Forecasting_ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Autoregressive Integrated Moving Averages\n",
    "\n",
    "Recall that an <strong>AR(1)</strong> model follows the formula\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = c + \\phi_{1}y_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "while an <strong>MA(1)</strong> model follows the formula\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = \\mu + \\theta_{1}\\varepsilon_{t-1} + \\varepsilon_{t}$\n",
    "\n",
    "where $c$ is a constant, $\\mu$ is the expectation of $y_{t}$ (often assumed to be zero), $\\phi_1$ is the AR lag coefficient, $\\theta_1$ (theta-sub-one) is the MA lag coefficient, and $\\varepsilon$ (epsilon) is white noise.\n",
    "\n",
    "An <strong>ARMA(1,1)</strong> model therefore follows\n",
    "\n",
    "&nbsp;&nbsp;&nbsp;&nbsp;$y_{t} = c + \\phi_{1}y_{t-1} + \\theta_{1}\\varepsilon_{t-1} + \\varepsilon_{t}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from math import sqrt\n",
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augmented Dickey Fuller Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "def adf_test(series):    \n",
    "    result = adfuller(series.dropna())  \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "    #for key,val in result[4].items():\n",
    "        #out[f'critical value ({key})']=val\n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data is stationary\")\n",
    "    else:\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data is non-stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>Births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1959-01-01</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-02</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-03</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-04</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-01-05</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-26</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-27</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-28</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-29</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959-04-30</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Births\n",
       "Date              \n",
       "1959-01-01      35\n",
       "1959-01-02      32\n",
       "1959-01-03      30\n",
       "1959-01-04      31\n",
       "1959-01-05      44\n",
       "...            ...\n",
       "1959-04-26      35\n",
       "1959-04-27      40\n",
       "1959-04-28      42\n",
       "1959-04-29      58\n",
       "1959-04-30      51\n",
       "\n",
       "[120 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load datasets\n",
    "df1 = pd.read_csv('daily-total-female-births.csv',index_col='Date',parse_dates=True)\n",
    "df1.index.freq = 'D'\n",
    "df1 = df1[:120]  # we only want the first four months\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Inventories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>1301161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-02-01</th>\n",
       "      <td>1307080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-03-01</th>\n",
       "      <td>1303978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-04-01</th>\n",
       "      <td>1319740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-05-01</th>\n",
       "      <td>1327294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>2127170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>2134172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>2144639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>2143001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>2158115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Inventories\n",
       "Date                   \n",
       "1997-01-01      1301161\n",
       "1997-02-01      1307080\n",
       "1997-03-01      1303978\n",
       "1997-04-01      1319740\n",
       "1997-05-01      1327294\n",
       "...                 ...\n",
       "2018-08-01      2127170\n",
       "2018-09-01      2134172\n",
       "2018-10-01      2144639\n",
       "2018-11-01      2143001\n",
       "2018-12-01      2158115\n",
       "\n",
       "[264 rows x 1 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('TradeInventories.csv',index_col='Date',parse_dates=True)\n",
    "df2.index.freq='MS'\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# EDA \n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['Inventories'].plot(figsize=(14,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run an ETS Decomposition (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "\n",
    "with plt.rc_context():\n",
    "    plt.rc(\"figure\", figsize=(16,8))\n",
    "    result = seasonal_decompose(df2['Inventories'], model='additive')  # model='add' also works\n",
    "    result.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check stationarity using Dickey-Fuller Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fail to reject the null hypothesis\n",
      "Data is non-stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df2['Inventories'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do first order differencing - (optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reject the null hypothesis\n",
      "Data is stationary\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.statespace.tools import diff\n",
    "df2['d1'] = diff(df2['Inventories'],k_diff=1)\n",
    "adf_test(df2['d1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set one year for testing\n",
    "train_data = df2.Inventories[:200]\n",
    "test_data = df2.Inventories[200:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train (200,)\n"
     ]
    }
   ],
   "source": [
    "print('Train', train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data.plot(legend=True,label='TRAIN')\n",
    "test_data.plot(legend=True,label='TEST',figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid search ARIMA parameters for a time series\n",
    "\n",
    "# evaluate an ARIMA model for a given order (p,d,q) and return RMSE\n",
    "def evaluate_arima_model(X, arima_order):\n",
    "# prepare training dataset\n",
    "    X = X.astype('float32')\n",
    "    train_size = int(len(X) * 0.70)\n",
    "    train, test = X[0:train_size], X[train_size:]\n",
    "    history = [x for x in train]\n",
    "# make predictions\n",
    "    predictions = list()\n",
    "    for t in range(len(test)):\n",
    "        model = ARIMA(history, order=arima_order)\n",
    "# model_fit = model.fit(disp=0)\n",
    "        model_fit = model.fit()\n",
    "        yhat = model_fit.forecast()[0]\n",
    "        predictions.append(yhat)\n",
    "        history.append(test[t])\n",
    "# calculate out of sample error\n",
    "    rmse = sqrt(mean_squared_error(test, predictions))\n",
    "    return rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# evaluate combinations of p, d and q values for an ARIMA model\n",
    "def evaluate_models(dataset, p_values, d_values, q_values):\n",
    "    dataset = dataset.astype('float32')\n",
    "    best_score, best_cfg = float('inf'), None\n",
    "    for p in p_values:\n",
    "        for d in d_values:\n",
    "            for q in q_values:\n",
    "                order = (p,d,q)\n",
    "                try:\n",
    "                    rmse = evaluate_arima_model(train, order)\n",
    "                    if rmse < best_score:\n",
    "                        best_score, best_cfg = rmse, order\n",
    "                    #print('ARIMA%s RMSE=%.3f' % (order,rmse))\n",
    "                except Exception as e:\n",
    "                    print(e)\n",
    "                    continue\n",
    "    print('Best ARIMA%s RMSE=%.3f' % (best_cfg, best_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0) RMSE=189211.882\n",
      "ARIMA(0, 0, 1) RMSE=96626.756\n",
      "ARIMA(0, 0, 2) RMSE=56732.100\n",
      "ARIMA(0, 0, 3) RMSE=36544.296\n",
      "ARIMA(0, 0, 4) RMSE=28678.846\n",
      "ARIMA(0, 1, 0) RMSE=5034.659\n",
      "ARIMA(0, 1, 1) RMSE=5018.624\n",
      "ARIMA(0, 1, 2) RMSE=5021.567\n",
      "ARIMA(0, 1, 3) RMSE=4991.656\n",
      "ARIMA(0, 1, 4) RMSE=4982.738\n",
      "ARIMA(0, 2, 0) RMSE=5239.043\n",
      "ARIMA(0, 2, 1) RMSE=5253.131\n",
      "ARIMA(0, 2, 2) RMSE=5210.447\n",
      "ARIMA(0, 2, 3) RMSE=5219.412\n",
      "ARIMA(0, 2, 4) RMSE=5216.567\n",
      "ARIMA(0, 3, 0) RMSE=8678.801\n",
      "ARIMA(0, 3, 1) RMSE=5252.637\n",
      "ARIMA(0, 3, 2) RMSE=8454.727\n",
      "ARIMA(0, 3, 3) RMSE=7946.027\n",
      "ARIMA(0, 3, 4) RMSE=5022.996\n",
      "ARIMA(0, 4, 0) RMSE=15737.375\n",
      "ARIMA(0, 4, 1) RMSE=16180.158\n",
      "ARIMA(0, 4, 2) RMSE=8028.450\n",
      "ARIMA(0, 4, 3) RMSE=7689.776\n",
      "ARIMA(0, 4, 4) RMSE=7399.766\n",
      "ARIMA(1, 0, 0) RMSE=5084.354\n",
      "ARIMA(1, 0, 1) RMSE=4707.966\n",
      "ARIMA(1, 0, 2) RMSE=4674.352\n",
      "ARIMA(1, 0, 3) RMSE=4396.971\n",
      "ARIMA(1, 0, 4) RMSE=4493.566\n",
      "ARIMA(1, 1, 0) RMSE=5018.367\n",
      "ARIMA(1, 1, 1) RMSE=4411.359\n",
      "ARIMA(1, 1, 2) RMSE=4451.254\n",
      "ARIMA(1, 1, 3) RMSE=4524.692\n",
      "ARIMA(1, 1, 4) RMSE=4438.086\n",
      "ARIMA(1, 2, 0) RMSE=5252.647\n",
      "ARIMA(1, 2, 1) RMSE=5262.465\n",
      "ARIMA(1, 2, 2) RMSE=5213.540\n",
      "ARIMA(1, 2, 3) RMSE=5220.524\n",
      "ARIMA(1, 2, 4) RMSE=5215.974\n",
      "ARIMA(1, 3, 0) RMSE=8785.589\n",
      "ARIMA(1, 3, 1) RMSE=4889.663\n",
      "ARIMA(1, 3, 2) RMSE=5330.105\n",
      "ARIMA(1, 3, 3) RMSE=5137.747\n",
      "ARIMA(1, 3, 4) RMSE=4951.305\n",
      "ARIMA(1, 4, 0) RMSE=16147.689\n",
      "ARIMA(1, 4, 1) RMSE=13538.270\n",
      "ARIMA(1, 4, 2) RMSE=8760.593\n",
      "ARIMA(1, 4, 3) RMSE=7077.115\n",
      "ARIMA(1, 4, 4) RMSE=7688.101\n",
      "ARIMA(2, 0, 0) RMSE=4518.785\n",
      "ARIMA(2, 0, 1) RMSE=4294.729\n",
      "LU decomposition error.\n",
      "ARIMA(2, 0, 3) RMSE=4273.750\n",
      "ARIMA(2, 0, 4) RMSE=4293.350\n",
      "ARIMA(2, 1, 0) RMSE=5021.027\n",
      "ARIMA(2, 1, 1) RMSE=5024.885\n",
      "ARIMA(2, 1, 2) RMSE=4090.448\n",
      "ARIMA(2, 1, 3) RMSE=4467.872\n",
      "ARIMA(2, 1, 4) RMSE=4528.339\n",
      "ARIMA(2, 2, 0) RMSE=5210.921\n",
      "ARIMA(2, 2, 1) RMSE=5213.892\n",
      "ARIMA(2, 2, 2) RMSE=5207.438\n",
      "ARIMA(2, 2, 3) RMSE=5216.645\n",
      "ARIMA(2, 2, 4) RMSE=5211.631\n",
      "ARIMA(2, 3, 0) RMSE=8500.199\n",
      "ARIMA(2, 3, 1) RMSE=4936.873\n",
      "ARIMA(2, 3, 2) RMSE=5081.071\n",
      "ARIMA(2, 3, 3) RMSE=5286.433\n",
      "ARIMA(2, 3, 4) RMSE=5092.490\n",
      "ARIMA(2, 4, 0) RMSE=14412.726\n",
      "ARIMA(2, 4, 1) RMSE=7051.551\n",
      "ARIMA(2, 4, 2) RMSE=7058.160\n",
      "ARIMA(2, 4, 3) RMSE=8727.443\n",
      "ARIMA(2, 4, 4) RMSE=8066.785\n",
      "ARIMA(3, 0, 0) RMSE=4419.816\n",
      "ARIMA(3, 0, 1) RMSE=4574.847\n",
      "ARIMA(3, 0, 2) RMSE=4085.221\n",
      "ARIMA(3, 0, 3) RMSE=4533.788\n",
      "ARIMA(3, 0, 4) RMSE=3874.354\n",
      "ARIMA(3, 1, 0) RMSE=4988.867\n",
      "ARIMA(3, 1, 1) RMSE=4525.329\n",
      "ARIMA(3, 1, 2) RMSE=4468.007\n",
      "ARIMA(3, 1, 3) RMSE=4424.362\n",
      "ARIMA(3, 1, 4) RMSE=4437.978\n",
      "ARIMA(3, 2, 0) RMSE=5220.019\n",
      "ARIMA(3, 2, 1) RMSE=5222.207\n",
      "ARIMA(3, 2, 2) RMSE=5215.395\n",
      "ARIMA(3, 2, 3) RMSE=5204.011\n",
      "ARIMA(3, 2, 4) RMSE=5215.934\n",
      "ARIMA(3, 3, 0) RMSE=8471.014\n",
      "ARIMA(3, 3, 1) RMSE=4961.472\n",
      "ARIMA(3, 3, 2) RMSE=5003.514\n",
      "ARIMA(3, 3, 3) RMSE=5186.815\n",
      "ARIMA(3, 3, 4) RMSE=5259.903\n",
      "ARIMA(3, 4, 0) RMSE=12665.182\n",
      "ARIMA(3, 4, 1) RMSE=7044.459\n",
      "ARIMA(3, 4, 2) RMSE=7047.429\n",
      "ARIMA(3, 4, 3) RMSE=7103.684\n",
      "ARIMA(3, 4, 4) RMSE=7592.087\n",
      "ARIMA(4, 0, 0) RMSE=4383.626\n",
      "ARIMA(4, 0, 1) RMSE=4253.736\n",
      "LU decomposition error.\n",
      "ARIMA(4, 0, 3) RMSE=4260.462\n",
      "ARIMA(4, 0, 4) RMSE=4283.550\n",
      "ARIMA(4, 1, 0) RMSE=4979.039\n",
      "ARIMA(4, 1, 1) RMSE=4438.609\n",
      "ARIMA(4, 1, 2) RMSE=4475.066\n",
      "ARIMA(4, 1, 3) RMSE=4493.932\n",
      "ARIMA(4, 1, 4) RMSE=4458.123\n",
      "ARIMA(4, 2, 0) RMSE=5216.536\n",
      "ARIMA(4, 2, 1) RMSE=5215.552\n",
      "ARIMA(4, 2, 2) RMSE=5222.256\n",
      "ARIMA(4, 2, 3) RMSE=5162.039\n",
      "ARIMA(4, 2, 4) RMSE=5202.872\n",
      "ARIMA(4, 3, 0) RMSE=8272.700\n",
      "ARIMA(4, 3, 1) RMSE=5016.599\n",
      "ARIMA(4, 3, 2) RMSE=5040.828\n",
      "ARIMA(4, 3, 3) RMSE=5123.011\n",
      "ARIMA(4, 3, 4) RMSE=5128.034\n",
      "ARIMA(4, 4, 0) RMSE=10513.991\n",
      "ARIMA(4, 4, 1) RMSE=6998.858\n",
      "ARIMA(4, 4, 2) RMSE=7052.473\n",
      "ARIMA(4, 4, 3) RMSE=22540945413.827\n",
      "ARIMA(4, 4, 4) RMSE=7108.581\n",
      "Best ARIMA(3, 0, 4) RMSE=3874.354\n"
     ]
    }
   ],
   "source": [
    "# evaluate parameters\n",
    "p_values = range(0, 5)\n",
    "d_values = range(0, 5)\n",
    "q_values = range(0, 5)\n",
    "evaluate_models(df2.Inventories.values, p_values, d_values, q_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Model based on the optimized values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>Inventories</td>   <th>  No. Observations:  </th>    <td>200</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(3, 0, 4)</td>  <th>  Log Likelihood     </th> <td>-1987.318</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 13 Jul 2022</td> <th>  AIC                </th> <td>3992.636</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:08:51</td>     <th>  BIC                </th> <td>4022.321</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-01-1997</td>    <th>  HQIC               </th> <td>4004.649</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 08-01-2013</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> 1.615e+06</td> <td> 1.83e-08</td> <td> 8.83e+13</td> <td> 0.000</td> <td> 1.61e+06</td> <td> 1.61e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    1.0856</td> <td>    0.078</td> <td>   13.941</td> <td> 0.000</td> <td>    0.933</td> <td>    1.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.7163</td> <td>    0.123</td> <td>    5.834</td> <td> 0.000</td> <td>    0.476</td> <td>    0.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>   -0.8032</td> <td>    0.062</td> <td>  -12.863</td> <td> 0.000</td> <td>   -0.926</td> <td>   -0.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>    0.4042</td> <td>    0.153</td> <td>    2.642</td> <td> 0.008</td> <td>    0.104</td> <td>    0.704</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.6254</td> <td>    0.113</td> <td>   -5.544</td> <td> 0.000</td> <td>   -0.847</td> <td>   -0.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>  <td>   -0.0141</td> <td>    0.096</td> <td>   -0.147</td> <td> 0.883</td> <td>   -0.201</td> <td>    0.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L4</th>  <td>    0.0157</td> <td>    0.098</td> <td>    0.159</td> <td> 0.873</td> <td>   -0.177</td> <td>    0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td> 2.488e+07</td> <td> 7.85e-09</td> <td> 3.17e+15</td> <td> 0.000</td> <td> 2.49e+07</td> <td> 2.49e+07</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>0.22</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.78</td> <th>  Prob(JB):          </th> <td>0.89</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.29</td> <th>  Skew:              </th> <td>0.06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.31</td> <th>  Kurtosis:          </th> <td>2.88</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 2.87e+30. Standard errors may be unstable."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:            Inventories   No. Observations:                  200\n",
       "Model:                 ARIMA(3, 0, 4)   Log Likelihood               -1987.318\n",
       "Date:                Wed, 13 Jul 2022   AIC                           3992.636\n",
       "Time:                        16:08:51   BIC                           4022.321\n",
       "Sample:                    01-01-1997   HQIC                          4004.649\n",
       "                         - 08-01-2013                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       1.615e+06   1.83e-08   8.83e+13      0.000    1.61e+06    1.61e+06\n",
       "ar.L1          1.0856      0.078     13.941      0.000       0.933       1.238\n",
       "ar.L2          0.7163      0.123      5.834      0.000       0.476       0.957\n",
       "ar.L3         -0.8032      0.062    -12.863      0.000      -0.926      -0.681\n",
       "ma.L1          0.4042      0.153      2.642      0.008       0.104       0.704\n",
       "ma.L2         -0.6254      0.113     -5.544      0.000      -0.847      -0.404\n",
       "ma.L3         -0.0141      0.096     -0.147      0.883      -0.201       0.173\n",
       "ma.L4          0.0157      0.098      0.159      0.873      -0.177       0.209\n",
       "sigma2      2.488e+07   7.85e-09   3.17e+15      0.000    2.49e+07    2.49e+07\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.08   Jarque-Bera (JB):                 0.22\n",
       "Prob(Q):                              0.78   Prob(JB):                         0.89\n",
       "Heteroskedasticity (H):               1.29   Skew:                             0.06\n",
       "Prob(H) (two-sided):                  0.31   Kurtosis:                         2.88\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 2.87e+30. Standard errors may be unstable.\n",
       "\"\"\""
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARIMA(train_data,order=(3, 0, 4))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use model to predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain predicted values\n",
    "start=len(train_data)\n",
    "end=len(df2)-1\n",
    "predictions = results.predict(start=start, end=end, dynamic=False, typ='levels')\n",
    "predictions = predictions.rename('ARIMA(3,0,4) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data)-len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2013-09-01    1848087\n",
       "2013-10-01    1860310\n",
       "2013-11-01    1871121\n",
       "2013-12-01    1878653\n",
       "2014-01-01    1885013\n",
       "               ...   \n",
       "2018-08-01    2127170\n",
       "2018-09-01    2134172\n",
       "2018-10-01    2144639\n",
       "2018-11-01    2143001\n",
       "2018-12-01    2158115\n",
       "Freq: MS, Name: Inventories, Length: 64, dtype: int64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-09-01    1.843791e+06\n",
       "2013-10-01    1.849337e+06\n",
       "2013-11-01    1.854112e+06\n",
       "2013-12-01    1.858722e+06\n",
       "2014-01-01    1.862619e+06\n",
       "                  ...     \n",
       "2018-08-01    1.799069e+06\n",
       "2018-09-01    1.796559e+06\n",
       "2018-10-01    1.794061e+06\n",
       "2018-11-01    1.791579e+06\n",
       "2018-12-01    1.789112e+06\n",
       "Freq: MS, Name: ARIMA(3,0,4) Predictions, Length: 64, dtype: float64"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted=1843790.835, expected=1848087\n",
      "predicted=1849336.865, expected=1860310\n",
      "predicted=1854111.88, expected=1871121\n",
      "predicted=1858721.57, expected=1878653\n",
      "predicted=1862618.851, expected=1885013\n",
      "predicted=1866315.983, expected=1892254\n",
      "predicted=1869418.354, expected=1896241\n",
      "predicted=1872303.939, expected=1903303\n",
      "predicted=1874688.939, expected=1910340\n",
      "predicted=1876852.975, expected=1914620\n",
      "predicted=1878592.71, expected=1923259\n",
      "predicted=1880115.653, expected=1930392\n",
      "predicted=1881276.822, expected=1933021\n",
      "predicted=1882230.789, expected=1940519\n",
      "predicted=1882874.831, expected=1945946\n",
      "predicted=1883324.601, expected=1947774\n",
      "predicted=1883507.915, expected=1953109\n",
      "predicted=1883511.763, expected=1966936\n",
      "predicted=1883285.976, expected=1974832\n",
      "predicted=1882896.385, expected=1986781\n",
      "predicted=1882308.645, expected=1991247\n",
      "predicted=1881572.921, expected=2003489\n",
      "predicted=1880666.193, expected=2008314\n",
      "predicted=1879626.991, expected=2014926\n",
      "predicted=1878440.36, expected=2026759\n",
      "predicted=1877136.152, expected=2035079\n",
      "predicted=1875705.113, expected=2034881\n",
      "predicted=1874170.593, expected=2040092\n",
      "predicted=1872527.333, expected=2042452\n",
      "predicted=1870793.782, expected=2042254\n",
      "predicted=1868967.445, expected=2051480\n",
      "predicted=1867063.053, expected=2057157\n",
      "predicted=1865079.992, expected=2057546\n",
      "predicted=1863030.148, expected=2056598\n",
      "predicted=1860914.156, expected=2052876\n",
      "predicted=1858741.709, expected=2055978\n",
      "predicted=1856514.239, expected=2059378\n",
      "predicted=1854239.733, expected=2056580\n",
      "predicted=1851920.099, expected=2065534\n",
      "predicted=1849561.983, expected=2070388\n",
      "predicted=1847167.548, expected=2070587\n",
      "predicted=1844742.371, expected=2070299\n",
      "predicted=1842288.718, expected=2071371\n",
      "predicted=1839811.302, expected=2068029\n",
      "predicted=1837312.388, expected=2072745\n",
      "predicted=1834795.99, expected=2080877\n",
      "predicted=1832264.298, expected=2085513\n",
      "predicted=1829720.748, expected=2097163\n",
      "predicted=1827167.416, expected=2097753\n",
      "predicted=1824607.255, expected=2095167\n",
      "predicted=1822042.197, expected=2099137\n",
      "predicted=1819474.792, expected=2103751\n",
      "predicted=1816906.815, expected=2110158\n",
      "predicted=1814340.469, expected=2118199\n",
      "predicted=1811777.371, expected=2112427\n",
      "predicted=1809219.424, expected=2112276\n",
      "predicted=1806668.086, expected=2111835\n",
      "predicted=1804125.003, expected=2109298\n",
      "predicted=1801591.479, expected=2119618\n",
      "predicted=1799068.936, expected=2127170\n",
      "predicted=1796558.528, expected=2134172\n",
      "predicted=1794061.483, expected=2144639\n",
      "predicted=1791578.814, expected=2143001\n",
      "predicted=1789111.576, expected=2158115\n"
     ]
    }
   ],
   "source": [
    "# Compare predictions to expected values\n",
    "for i in range(len(test_data)):\n",
    "    print(f\"predicted={predictions[i]:<.10}, expected={test_data[i]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions against known values\n",
    "title = 'Real Manufacturing and Trade Inventories'\n",
    "ylabel='Inventories'\n",
    "xlabel='' \n",
    "\n",
    "train_data.plot(legend=True,label='TRAIN' ,figsize=(16,6),title=title)\n",
    "test_data.plot(legend=True,label='Test',figsize=(16,6),title=title)\n",
    "predictions.plot(legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#train_data['Thousands of Passengers'].plot(legend=True,label='TRAIN')\n",
    "test_data.plot(legend=True,label='TEST',figsize=(16,8))\n",
    "predictions.plot(legend=True,label='PREDICTION',xlim=['2013-09-01','2018-12-01'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the Model"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#df['returns'] = df.market_value.pct_change(1)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41951060391.44"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "error = mean_squared_error(test_data, predictions)\n",
    "error.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204819.58"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "error = rmse(test_data, predictions)\n",
    "error.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrain the model on the full data, and forecast the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ARIMA(df2['Inventories'],order=(3,0,4))\n",
    "results = model.fit()\n",
    "fcast = results.predict(len(df2),len(df2)+11,typ='levels').rename('ARIMA(3,0,4) Forecast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot predictions against known values\n",
    "title = 'Real Manufacturing and Trade Inventories'\n",
    "ylabel='Inventories'\n",
    "xlabel='Period' \n",
    "\n",
    "train_data.plot(legend=True,label='Train',figsize=(12,6),title=title)\n",
    "test_data.plot(legend=True,label='Test',figsize=(12,6),title=title)\n",
    "fcast.plot(legend=True)\n",
    "predictions.plot(legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}