VARMA_model_Covid19_India.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "#load forecasting tools\n",
    "from statsmodels.tsa.statespace.varmax import VARMAX, VARMAXResults\n",
    "from pmdarima import auto_arima\n",
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set seed\n",
    "import random\n",
    "random.seed(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-29</th>\n",
       "      <td>1024</td>\n",
       "      <td>27</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-30</th>\n",
       "      <td>1251</td>\n",
       "      <td>32</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>1397</td>\n",
       "      <td>35</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>1998</td>\n",
       "      <td>58</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>2543</td>\n",
       "      <td>72</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cases  Deaths  Recovered\n",
       "Date                                \n",
       "2020-03-29   1024      27         95\n",
       "2020-03-30   1251      32        102\n",
       "2020-03-31   1397      35        123\n",
       "2020-04-01   1998      58        148\n",
       "2020-04-02   2543      72        191"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define dataset \n",
    "series = pd.read_csv(r\"C:\\Users\\IFMRUSER\\Desktop\\Deep Learning\\India_covid19.csv\", index_col = 'Date' , parse_dates = True)\n",
    "#taking the dataset when India crossed 1000 mark \n",
    "dfo=series.iloc[67:]\n",
    "dfo.index.frequency = 'D'\n",
    "dfo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14151088>"
      ]
     },
     "execution_count": 209,
     "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": [
    "#ploting the data\n",
    "dfo.columns = ['Cases', 'Deaths', 'Recovered']\n",
    "dfo.plot(figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(maxiter=1000, method='lbfgs', order=(0, 2, 1), out_of_sample_size=0,\n",
       "      scoring='mse', scoring_args=None, seasonal_order=(0, 0, 0, 0),\n",
       "      start_params=None, suppress_warnings=False, trend=None,\n",
       "      with_intercept=True)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying auto_arima() to find p,d,q\n",
    "auto_arima(dfo['Cases'],maxiter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(maxiter=1000, method='lbfgs', order=(0, 2, 1), out_of_sample_size=0,\n",
       "      scoring='mse', scoring_args=None, seasonal_order=(0, 0, 0, 0),\n",
       "      start_params=None, suppress_warnings=False, trend=None,\n",
       "      with_intercept=True)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying auto_arima() to find p,d,q\n",
    "auto_arima(dfo['Deaths'],maxiter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ARIMA(maxiter=1000, method='lbfgs', order=(2, 2, 0), out_of_sample_size=0,\n",
       "      scoring='mse', scoring_args=None, seasonal_order=(0, 0, 0, 0),\n",
       "      start_params=None, suppress_warnings=False, trend=None,\n",
       "      with_intercept=True)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying auto_arima() to find p,d,q\n",
    "auto_arima(dfo['Recovered'],maxiter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-31</th>\n",
       "      <td>-81.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-01</th>\n",
       "      <td>455.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-02</th>\n",
       "      <td>-56.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>-521.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>491.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cases  Deaths  Recovered\n",
       "Date                                \n",
       "2020-03-31  -81.0    -2.0       14.0\n",
       "2020-04-01  455.0    20.0        4.0\n",
       "2020-04-02  -56.0    -9.0       18.0\n",
       "2020-04-03 -521.0   -14.0      -42.0\n",
       "2020-04-04  491.0    14.0       36.0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2nd order difference for Stationarty as derived from auto_arima()\n",
    "df_transformed = dfo.diff().diff()\n",
    "df_transformed = df_transformed.dropna()\n",
    "df_transformed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(44, 3)\n",
      "(5, 3)\n"
     ]
    }
   ],
   "source": [
    "#split data into test and train\n",
    "nobs=5\n",
    "len(df_transformed)\n",
    "train, test = df_transformed[0:-nobs], df_transformed[-nobs:]\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>   <td>['Cases', 'Deaths', 'Recovered']</td> <th>  No. Observations:  </th>    <td>44</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                      <td>VARMA(2,1)</td>            <th>  Log Likelihood     </th> <td>-768.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                            <td>+ intercept</td>           <th>  AIC                </th> <td>1608.497</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Tue, 19 May 2020</td>         <th>  BIC                </th> <td>1672.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>12:16:10</td>             <th>  HQIC               </th> <td>1632.317</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                     <td>03-31-2020</td>            <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>- 05-13-2020</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",
       "  <th>Ljung-Box (Q):</th>          <td>34.01, 24.13, 34.93</td> <th>  Jarque-Bera (JB):  </th> <td>6.38, 0.03, 1.52</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                 <td>0.74, 0.98, 0.70</td>   <th>  Prob(JB):          </th> <td>0.04, 0.98, 0.47</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th>  <td>1.89, 4.24, 5.19</td>   <th>  Skew:              </th> <td>0.86, -0.03, 0.28</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>     <td>0.23, 0.01, 0.00</td>   <th>  Kurtosis:          </th> <td>3.70, 2.87, 2.29</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation Cases</caption>\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>       <td>  179.0538</td> <td>  316.386</td> <td>    0.566</td> <td> 0.571</td> <td> -441.051</td> <td>  799.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Cases</th>        <td>   -0.3036</td> <td>    1.334</td> <td>   -0.228</td> <td> 0.820</td> <td>   -2.918</td> <td>    2.311</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Deaths</th>       <td>   -2.1216</td> <td>   30.970</td> <td>   -0.069</td> <td> 0.945</td> <td>  -62.821</td> <td>   58.578</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Recovered</th>    <td>   -0.9312</td> <td>    4.377</td> <td>   -0.213</td> <td> 0.832</td> <td>   -9.510</td> <td>    7.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Cases</th>        <td>   -0.1781</td> <td>    0.933</td> <td>   -0.191</td> <td> 0.849</td> <td>   -2.007</td> <td>    1.651</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Deaths</th>       <td>    5.4091</td> <td>   20.226</td> <td>    0.267</td> <td> 0.789</td> <td>  -34.232</td> <td>   45.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Recovered</th>    <td>   -1.1324</td> <td>    4.316</td> <td>   -0.262</td> <td> 0.793</td> <td>   -9.591</td> <td>    7.326</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Cases)</th>     <td>   -0.3829</td> <td>    2.345</td> <td>   -0.163</td> <td> 0.870</td> <td>   -4.979</td> <td>    4.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Deaths)</th>    <td>    4.2386</td> <td>   17.990</td> <td>    0.236</td> <td> 0.814</td> <td>  -31.021</td> <td>   39.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Recovered)</th> <td>    1.4117</td> <td>    4.989</td> <td>    0.283</td> <td> 0.777</td> <td>   -8.367</td> <td>   11.190</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation Deaths</caption>\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>       <td>   10.7113</td> <td>   17.043</td> <td>    0.628</td> <td> 0.530</td> <td>  -22.693</td> <td>   44.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Cases</th>        <td>   -0.0318</td> <td>    0.094</td> <td>   -0.340</td> <td> 0.734</td> <td>   -0.215</td> <td>    0.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Deaths</th>       <td>   -0.6614</td> <td>    1.641</td> <td>   -0.403</td> <td> 0.687</td> <td>   -3.877</td> <td>    2.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Recovered</th>    <td>   -0.0151</td> <td>    0.185</td> <td>   -0.082</td> <td> 0.935</td> <td>   -0.378</td> <td>    0.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Cases</th>        <td>   -0.0165</td> <td>    0.038</td> <td>   -0.436</td> <td> 0.663</td> <td>   -0.091</td> <td>    0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Deaths</th>       <td>   -0.3771</td> <td>    1.795</td> <td>   -0.210</td> <td> 0.834</td> <td>   -3.895</td> <td>    3.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Recovered</th>    <td>   -0.0380</td> <td>    0.263</td> <td>   -0.145</td> <td> 0.885</td> <td>   -0.553</td> <td>    0.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Cases)</th>     <td>    0.0436</td> <td>    0.126</td> <td>    0.345</td> <td> 0.730</td> <td>   -0.204</td> <td>    0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Deaths)</th>    <td>   -0.0967</td> <td>    1.481</td> <td>   -0.065</td> <td> 0.948</td> <td>   -2.999</td> <td>    2.805</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Recovered)</th> <td>   -0.0033</td> <td>    0.190</td> <td>   -0.017</td> <td> 0.986</td> <td>   -0.375</td> <td>    0.369</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Results for equation Recovered</caption>\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th>       <td>   83.9034</td> <td>  114.120</td> <td>    0.735</td> <td> 0.462</td> <td> -139.768</td> <td>  307.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Cases</th>        <td>    0.2341</td> <td>    0.684</td> <td>    0.342</td> <td> 0.732</td> <td>   -1.107</td> <td>    1.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Deaths</th>       <td>   -0.6093</td> <td>   12.699</td> <td>   -0.048</td> <td> 0.962</td> <td>  -25.499</td> <td>   24.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.Recovered</th>    <td>   -1.5202</td> <td>    1.608</td> <td>   -0.945</td> <td> 0.345</td> <td>   -4.672</td> <td>    1.632</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Cases</th>        <td>    0.0816</td> <td>    0.333</td> <td>    0.245</td> <td> 0.806</td> <td>   -0.571</td> <td>    0.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Deaths</th>       <td>    0.3198</td> <td>    9.288</td> <td>    0.034</td> <td> 0.973</td> <td>  -17.884</td> <td>   18.523</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L2.Recovered</th>    <td>   -0.6703</td> <td>    1.763</td> <td>   -0.380</td> <td> 0.704</td> <td>   -4.126</td> <td>    2.785</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Cases)</th>     <td>   -0.3090</td> <td>    0.897</td> <td>   -0.345</td> <td> 0.730</td> <td>   -2.067</td> <td>    1.449</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Deaths)</th>    <td>    1.4547</td> <td>   10.335</td> <td>    0.141</td> <td> 0.888</td> <td>  -18.802</td> <td>   21.712</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>L1.e(Recovered)</th> <td>    1.2656</td> <td>    1.599</td> <td>    0.792</td> <td> 0.429</td> <td>   -1.867</td> <td>    4.399</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Error covariance matrix</caption>\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>sqrt.var.Cases</th>            <td>  372.8939</td> <td>  113.066</td> <td>    3.298</td> <td> 0.001</td> <td>  151.288</td> <td>  594.500</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.Cases.Deaths</th>     <td>   10.4069</td> <td>   12.263</td> <td>    0.849</td> <td> 0.396</td> <td>  -13.627</td> <td>   34.441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.Deaths</th>           <td>   14.7001</td> <td>    6.252</td> <td>    2.351</td> <td> 0.019</td> <td>    2.446</td> <td>   26.954</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.Cases.Recovered</th>  <td>   44.3854</td> <td>   84.173</td> <td>    0.527</td> <td> 0.598</td> <td> -120.590</td> <td>  209.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.cov.Deaths.Recovered</th> <td>   27.1292</td> <td>  103.709</td> <td>    0.262</td> <td> 0.794</td> <td> -176.137</td> <td>  230.396</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sqrt.var.Recovered</th>        <td>  107.1335</td> <td>   72.503</td> <td>    1.478</td> <td> 0.140</td> <td>  -34.969</td> <td>  249.236</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",
       "                                  Statespace Model Results                                  \n",
       "============================================================================================\n",
       "Dep. Variable:     ['Cases', 'Deaths', 'Recovered']   No. Observations:                   44\n",
       "Model:                                   VARMA(2,1)   Log Likelihood                -768.249\n",
       "                                        + intercept   AIC                           1608.497\n",
       "Date:                              Tue, 19 May 2020   BIC                           1672.728\n",
       "Time:                                      12:16:10   HQIC                          1632.317\n",
       "Sample:                                  03-31-2020                                         \n",
       "                                       - 05-13-2020                                         \n",
       "Covariance Type:                                opg                                         \n",
       "====================================================================================\n",
       "Ljung-Box (Q):          34.01, 24.13, 34.93   Jarque-Bera (JB):     6.38, 0.03, 1.52\n",
       "Prob(Q):                   0.74, 0.98, 0.70   Prob(JB):             0.04, 0.98, 0.47\n",
       "Heteroskedasticity (H):    1.89, 4.24, 5.19   Skew:                0.86, -0.03, 0.28\n",
       "Prob(H) (two-sided):       0.23, 0.01, 0.00   Kurtosis:             3.70, 2.87, 2.29\n",
       "                             Results for equation Cases                            \n",
       "===================================================================================\n",
       "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "intercept         179.0538    316.386      0.566      0.571    -441.051     799.159\n",
       "L1.Cases           -0.3036      1.334     -0.228      0.820      -2.918       2.311\n",
       "L1.Deaths          -2.1216     30.970     -0.069      0.945     -62.821      58.578\n",
       "L1.Recovered       -0.9312      4.377     -0.213      0.832      -9.510       7.647\n",
       "L2.Cases           -0.1781      0.933     -0.191      0.849      -2.007       1.651\n",
       "L2.Deaths           5.4091     20.226      0.267      0.789     -34.232      45.050\n",
       "L2.Recovered       -1.1324      4.316     -0.262      0.793      -9.591       7.326\n",
       "L1.e(Cases)        -0.3829      2.345     -0.163      0.870      -4.979       4.213\n",
       "L1.e(Deaths)        4.2386     17.990      0.236      0.814     -31.021      39.498\n",
       "L1.e(Recovered)     1.4117      4.989      0.283      0.777      -8.367      11.190\n",
       "                            Results for equation Deaths                            \n",
       "===================================================================================\n",
       "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "intercept          10.7113     17.043      0.628      0.530     -22.693      44.115\n",
       "L1.Cases           -0.0318      0.094     -0.340      0.734      -0.215       0.152\n",
       "L1.Deaths          -0.6614      1.641     -0.403      0.687      -3.877       2.554\n",
       "L1.Recovered       -0.0151      0.185     -0.082      0.935      -0.378       0.348\n",
       "L2.Cases           -0.0165      0.038     -0.436      0.663      -0.091       0.058\n",
       "L2.Deaths          -0.3771      1.795     -0.210      0.834      -3.895       3.141\n",
       "L2.Recovered       -0.0380      0.263     -0.145      0.885      -0.553       0.477\n",
       "L1.e(Cases)         0.0436      0.126      0.345      0.730      -0.204       0.291\n",
       "L1.e(Deaths)       -0.0967      1.481     -0.065      0.948      -2.999       2.805\n",
       "L1.e(Recovered)    -0.0033      0.190     -0.017      0.986      -0.375       0.369\n",
       "                           Results for equation Recovered                          \n",
       "===================================================================================\n",
       "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "intercept          83.9034    114.120      0.735      0.462    -139.768     307.575\n",
       "L1.Cases            0.2341      0.684      0.342      0.732      -1.107       1.576\n",
       "L1.Deaths          -0.6093     12.699     -0.048      0.962     -25.499      24.281\n",
       "L1.Recovered       -1.5202      1.608     -0.945      0.345      -4.672       1.632\n",
       "L2.Cases            0.0816      0.333      0.245      0.806      -0.571       0.735\n",
       "L2.Deaths           0.3198      9.288      0.034      0.973     -17.884      18.523\n",
       "L2.Recovered       -0.6703      1.763     -0.380      0.704      -4.126       2.785\n",
       "L1.e(Cases)        -0.3090      0.897     -0.345      0.730      -2.067       1.449\n",
       "L1.e(Deaths)        1.4547     10.335      0.141      0.888     -18.802      21.712\n",
       "L1.e(Recovered)     1.2656      1.599      0.792      0.429      -1.867       4.399\n",
       "                                   Error covariance matrix                                   \n",
       "=============================================================================================\n",
       "                                coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "sqrt.var.Cases              372.8939    113.066      3.298      0.001     151.288     594.500\n",
       "sqrt.cov.Cases.Deaths        10.4069     12.263      0.849      0.396     -13.627      34.441\n",
       "sqrt.var.Deaths              14.7001      6.252      2.351      0.019       2.446      26.954\n",
       "sqrt.cov.Cases.Recovered     44.3854     84.173      0.527      0.598    -120.590     209.361\n",
       "sqrt.cov.Deaths.Recovered    27.1292    103.709      0.262      0.794    -176.137     230.396\n",
       "sqrt.var.Recovered          107.1335     72.503      1.478      0.140     -34.969     249.236\n",
       "=============================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying the VARMA model\n",
    "model = VARMAX(train, order = (2,1), trend = 'c' )\n",
    "results = model.fit(maxiter = 1000, disp = False)\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
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       "      <th>2020-05-14</th>\n",
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       "      <th>2020-05-16</th>\n",
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       "      <td>8.157641</td>\n",
       "      <td>33.557459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-17</th>\n",
       "      <td>259.234839</td>\n",
       "      <td>5.257614</td>\n",
       "      <td>-17.121125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-18</th>\n",
       "      <td>148.535105</td>\n",
       "      <td>-1.644062</td>\n",
       "      <td>130.454967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Cases     Deaths   Recovered\n",
       "2020-05-14  245.768343 -39.772960   73.418951\n",
       "2020-05-15   35.585196  14.343083    5.308968\n",
       "2020-05-16 -209.184825   8.157641   33.557459\n",
       "2020-05-17  259.234839   5.257614  -17.121125\n",
       "2020-05-18  148.535105  -1.644062  130.454967"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing the forecasted values but this will be in 2nd order dfference\n",
    "df_forcast = results.forecast(5)\n",
    "df_forcast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Inversing the 2nd order difference\n",
    "\n",
    "# Add the most recent first difference from the training set to the forecast cumulative sum\n",
    "df_forcast['Cases1d'] = (dfo['Cases'].iloc[-nobs-1]-dfo['Cases'].iloc[-nobs-2]) + df_forcast['Cases'].cumsum()\n",
    "df_forcast['Deaths1d'] = (dfo['Deaths'].iloc[-nobs-1]-dfo['Deaths'].iloc[-nobs-2]) + df_forcast['Deaths'].cumsum()\n",
    "df_forcast['Recovered1d'] = (dfo['Recovered'].iloc[-nobs-1]-dfo['Recovered'].iloc[-nobs-2]) + df_forcast['Recovered'].cumsum()\n",
    "\n",
    "\n",
    "# Now build the forecast values from the first difference set\n",
    "df_forcast['CasesForecast'] = dfo['Cases'].iloc[-nobs-1] + df_forcast['Cases1d'].cumsum()\n",
    "df_forcast['DeathsForecast'] = dfo['Deaths'].iloc[-nobs-1] + df_forcast['Deaths1d'].cumsum()\n",
    "df_forcast['RecoveredForecast'] = dfo['Recovered'].iloc[-nobs-1] + df_forcast['Recovered1d'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "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>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Cases1d</th>\n",
       "      <th>Deaths1d</th>\n",
       "      <th>Recovered1d</th>\n",
       "      <th>CasesForecast</th>\n",
       "      <th>DeathsForecast</th>\n",
       "      <th>RecoveredForecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>245.768343</td>\n",
       "      <td>-39.772960</td>\n",
       "      <td>73.418951</td>\n",
       "      <td>4008.768343</td>\n",
       "      <td>96.227040</td>\n",
       "      <td>2053.418951</td>\n",
       "      <td>82063.768343</td>\n",
       "      <td>2647.227040</td>\n",
       "      <td>28453.418951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-15</th>\n",
       "      <td>35.585196</td>\n",
       "      <td>14.343083</td>\n",
       "      <td>5.308968</td>\n",
       "      <td>4044.353540</td>\n",
       "      <td>110.570124</td>\n",
       "      <td>2058.727920</td>\n",
       "      <td>86108.121883</td>\n",
       "      <td>2757.797164</td>\n",
       "      <td>30512.146871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-16</th>\n",
       "      <td>-209.184825</td>\n",
       "      <td>8.157641</td>\n",
       "      <td>33.557459</td>\n",
       "      <td>3835.168715</td>\n",
       "      <td>118.727764</td>\n",
       "      <td>2092.285379</td>\n",
       "      <td>89943.290598</td>\n",
       "      <td>2876.524928</td>\n",
       "      <td>32604.432250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-17</th>\n",
       "      <td>259.234839</td>\n",
       "      <td>5.257614</td>\n",
       "      <td>-17.121125</td>\n",
       "      <td>4094.403553</td>\n",
       "      <td>123.985379</td>\n",
       "      <td>2075.164254</td>\n",
       "      <td>94037.694151</td>\n",
       "      <td>3000.510307</td>\n",
       "      <td>34679.596503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-18</th>\n",
       "      <td>148.535105</td>\n",
       "      <td>-1.644062</td>\n",
       "      <td>130.454967</td>\n",
       "      <td>4242.938658</td>\n",
       "      <td>122.341317</td>\n",
       "      <td>2205.619221</td>\n",
       "      <td>98280.632809</td>\n",
       "      <td>3122.851624</td>\n",
       "      <td>36885.215724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Cases     Deaths   Recovered      Cases1d    Deaths1d  \\\n",
       "2020-05-14  245.768343 -39.772960   73.418951  4008.768343   96.227040   \n",
       "2020-05-15   35.585196  14.343083    5.308968  4044.353540  110.570124   \n",
       "2020-05-16 -209.184825   8.157641   33.557459  3835.168715  118.727764   \n",
       "2020-05-17  259.234839   5.257614  -17.121125  4094.403553  123.985379   \n",
       "2020-05-18  148.535105  -1.644062  130.454967  4242.938658  122.341317   \n",
       "\n",
       "            Recovered1d  CasesForecast  DeathsForecast  RecoveredForecast  \n",
       "2020-05-14  2053.418951   82063.768343     2647.227040       28453.418951  \n",
       "2020-05-15  2058.727920   86108.121883     2757.797164       30512.146871  \n",
       "2020-05-16  2092.285379   89943.290598     2876.524928       32604.432250  \n",
       "2020-05-17  2075.164254   94037.694151     3000.510307       34679.596503  \n",
       "2020-05-18  2205.619221   98280.632809     3122.851624       36885.215724  "
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lets now see the forecasted values\n",
    "df_forcast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x142fb048>"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting the forecasted values against actual\n",
    "pd.concat([dfo.iloc[-nobs:],df_forcast[['CasesForecast','DeathsForecast','RecoveredForecast']]],axis=1)\n",
    "Final = pd.concat([dfo.iloc[-nobs:],df_forcast[['CasesForecast','DeathsForecast','RecoveredForecast']]],axis=1)\n",
    "Final.plot(figsize = (20,12))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cases RMSE: 1229.183\n",
      "Deaths RMSE: 18.736\n",
      "Recovered RMSE: 1606.815\n"
     ]
    }
   ],
   "source": [
    "#Evaluating the model\n",
    "RMSE1 = rmse(dfo.iloc[-nobs:,0], df_forcast.iloc[:,6])\n",
    "RMSE2 = rmse(dfo.iloc[-nobs:,1], df_forcast.iloc[:,7])\n",
    "RMSE3 = rmse(dfo.iloc[-nobs:,2], df_forcast.iloc[:,8])\n",
    "\n",
    "print(f'Cases RMSE: {RMSE1:.3f}')\n",
    "print(f'Deaths RMSE: {RMSE2:.3f}')\n",
    "print(f'Recovered RMSE: {RMSE3:.3f}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}