arima_india.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3826aae7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "# import lazypredict\n",
    "\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import statsmodels.tsa.stattools as st\n",
    "from statsmodels.tsa.arima_model import ARMA\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tools.eval_measures import rmse, aic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "771a8a66",
   "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>Country</th>\n",
       "      <th>ISO 3166-1 alpha-3</th>\n",
       "      <th>Total</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>291.711364</td>\n",
       "      <td>197.337053</td>\n",
       "      <td>83.176464</td>\n",
       "      <td>2.487856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>314.966469</td>\n",
       "      <td>210.305446</td>\n",
       "      <td>89.713040</td>\n",
       "      <td>2.883568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>325.380295</td>\n",
       "      <td>213.123221</td>\n",
       "      <td>94.406624</td>\n",
       "      <td>4.360160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>352.199157</td>\n",
       "      <td>232.519456</td>\n",
       "      <td>99.169824</td>\n",
       "      <td>5.243184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>361.559743</td>\n",
       "      <td>231.492925</td>\n",
       "      <td>106.104774</td>\n",
       "      <td>6.170118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>397.592455</td>\n",
       "      <td>253.289252</td>\n",
       "      <td>116.759685</td>\n",
       "      <td>7.199698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>426.309754</td>\n",
       "      <td>273.171658</td>\n",
       "      <td>121.857312</td>\n",
       "      <td>10.310496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>455.343444</td>\n",
       "      <td>291.856287</td>\n",
       "      <td>127.671122</td>\n",
       "      <td>12.776272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>491.692596</td>\n",
       "      <td>317.172040</td>\n",
       "      <td>133.615088</td>\n",
       "      <td>15.205600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>540.651493</td>\n",
       "      <td>339.625896</td>\n",
       "      <td>150.714021</td>\n",
       "      <td>18.517739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>577.996538</td>\n",
       "      <td>366.514068</td>\n",
       "      <td>158.166611</td>\n",
       "      <td>21.097187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>615.365508</td>\n",
       "      <td>393.043111</td>\n",
       "      <td>166.422544</td>\n",
       "      <td>23.563184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>655.447568</td>\n",
       "      <td>411.415002</td>\n",
       "      <td>188.054800</td>\n",
       "      <td>27.256496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>677.299816</td>\n",
       "      <td>433.121813</td>\n",
       "      <td>186.878656</td>\n",
       "      <td>26.996352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>716.268589</td>\n",
       "      <td>455.720366</td>\n",
       "      <td>198.955200</td>\n",
       "      <td>28.996896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>761.449242</td>\n",
       "      <td>471.723186</td>\n",
       "      <td>216.281275</td>\n",
       "      <td>39.050735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>825.200806</td>\n",
       "      <td>504.498913</td>\n",
       "      <td>243.160345</td>\n",
       "      <td>39.746906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>858.935134</td>\n",
       "      <td>527.722798</td>\n",
       "      <td>244.201936</td>\n",
       "      <td>46.023504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>876.952083</td>\n",
       "      <td>524.125992</td>\n",
       "      <td>262.892000</td>\n",
       "      <td>47.595360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>950.950878</td>\n",
       "      <td>560.528326</td>\n",
       "      <td>300.510056</td>\n",
       "      <td>42.315536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>978.103843</td>\n",
       "      <td>572.876810</td>\n",
       "      <td>312.286549</td>\n",
       "      <td>43.872736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>991.731661</td>\n",
       "      <td>582.083456</td>\n",
       "      <td>316.115271</td>\n",
       "      <td>43.568933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1022.175223</td>\n",
       "      <td>598.598121</td>\n",
       "      <td>324.587833</td>\n",
       "      <td>47.133696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1058.725338</td>\n",
       "      <td>625.890755</td>\n",
       "      <td>329.936696</td>\n",
       "      <td>49.987952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1124.517704</td>\n",
       "      <td>669.968548</td>\n",
       "      <td>347.518127</td>\n",
       "      <td>49.584753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1184.926501</td>\n",
       "      <td>721.469674</td>\n",
       "      <td>351.646194</td>\n",
       "      <td>50.438624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1258.643267</td>\n",
       "      <td>773.565357</td>\n",
       "      <td>368.891165</td>\n",
       "      <td>49.442016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1356.953125</td>\n",
       "      <td>842.478182</td>\n",
       "      <td>391.027790</td>\n",
       "      <td>52.695648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1461.518268</td>\n",
       "      <td>910.796506</td>\n",
       "      <td>412.316303</td>\n",
       "      <td>63.046601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1611.416152</td>\n",
       "      <td>983.948220</td>\n",
       "      <td>431.609933</td>\n",
       "      <td>112.684767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1676.495006</td>\n",
       "      <td>1015.983089</td>\n",
       "      <td>438.373461</td>\n",
       "      <td>134.244240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1778.472509</td>\n",
       "      <td>1089.807084</td>\n",
       "      <td>458.909201</td>\n",
       "      <td>136.364239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>1962.594350</td>\n",
       "      <td>1244.606043</td>\n",
       "      <td>488.554125</td>\n",
       "      <td>125.604798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2037.415492</td>\n",
       "      <td>1319.998650</td>\n",
       "      <td>495.642138</td>\n",
       "      <td>110.689227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2187.343005</td>\n",
       "      <td>1447.354894</td>\n",
       "      <td>511.166221</td>\n",
       "      <td>109.467500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2270.766191</td>\n",
       "      <td>1487.461399</td>\n",
       "      <td>554.788779</td>\n",
       "      <td>106.870633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2383.815964</td>\n",
       "      <td>1529.990408</td>\n",
       "      <td>613.165557</td>\n",
       "      <td>113.622467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2434.867906</td>\n",
       "      <td>1560.988150</td>\n",
       "      <td>631.087430</td>\n",
       "      <td>118.922003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2600.446463</td>\n",
       "      <td>1677.600364</td>\n",
       "      <td>655.190293</td>\n",
       "      <td>126.201229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2626.459473</td>\n",
       "      <td>1677.653708</td>\n",
       "      <td>674.011463</td>\n",
       "      <td>128.675994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2445.011942</td>\n",
       "      <td>1588.060207</td>\n",
       "      <td>603.602160</td>\n",
       "      <td>127.635380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>2709.683625</td>\n",
       "      <td>1802.311897</td>\n",
       "      <td>622.148121</td>\n",
       "      <td>133.450811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country ISO 3166-1 alpha-3        Total         Coal         Oil  \\\n",
       "Year                                                                    \n",
       "1980   India                IND   291.711364   197.337053   83.176464   \n",
       "1981   India                IND   314.966469   210.305446   89.713040   \n",
       "1982   India                IND   325.380295   213.123221   94.406624   \n",
       "1983   India                IND   352.199157   232.519456   99.169824   \n",
       "1984   India                IND   361.559743   231.492925  106.104774   \n",
       "1985   India                IND   397.592455   253.289252  116.759685   \n",
       "1986   India                IND   426.309754   273.171658  121.857312   \n",
       "1987   India                IND   455.343444   291.856287  127.671122   \n",
       "1988   India                IND   491.692596   317.172040  133.615088   \n",
       "1989   India                IND   540.651493   339.625896  150.714021   \n",
       "1990   India                IND   577.996538   366.514068  158.166611   \n",
       "1991   India                IND   615.365508   393.043111  166.422544   \n",
       "1992   India                IND   655.447568   411.415002  188.054800   \n",
       "1993   India                IND   677.299816   433.121813  186.878656   \n",
       "1994   India                IND   716.268589   455.720366  198.955200   \n",
       "1995   India                IND   761.449242   471.723186  216.281275   \n",
       "1996   India                IND   825.200806   504.498913  243.160345   \n",
       "1997   India                IND   858.935134   527.722798  244.201936   \n",
       "1998   India                IND   876.952083   524.125992  262.892000   \n",
       "1999   India                IND   950.950878   560.528326  300.510056   \n",
       "2000   India                IND   978.103843   572.876810  312.286549   \n",
       "2001   India                IND   991.731661   582.083456  316.115271   \n",
       "2002   India                IND  1022.175223   598.598121  324.587833   \n",
       "2003   India                IND  1058.725338   625.890755  329.936696   \n",
       "2004   India                IND  1124.517704   669.968548  347.518127   \n",
       "2005   India                IND  1184.926501   721.469674  351.646194   \n",
       "2006   India                IND  1258.643267   773.565357  368.891165   \n",
       "2007   India                IND  1356.953125   842.478182  391.027790   \n",
       "2008   India                IND  1461.518268   910.796506  412.316303   \n",
       "2009   India                IND  1611.416152   983.948220  431.609933   \n",
       "2010   India                IND  1676.495006  1015.983089  438.373461   \n",
       "2011   India                IND  1778.472509  1089.807084  458.909201   \n",
       "2012   India                IND  1962.594350  1244.606043  488.554125   \n",
       "2013   India                IND  2037.415492  1319.998650  495.642138   \n",
       "2014   India                IND  2187.343005  1447.354894  511.166221   \n",
       "2015   India                IND  2270.766191  1487.461399  554.788779   \n",
       "2016   India                IND  2383.815964  1529.990408  613.165557   \n",
       "2017   India                IND  2434.867906  1560.988150  631.087430   \n",
       "2018   India                IND  2600.446463  1677.600364  655.190293   \n",
       "2019   India                IND  2626.459473  1677.653708  674.011463   \n",
       "2020   India                IND  2445.011942  1588.060207  603.602160   \n",
       "2021   India                IND  2709.683625  1802.311897  622.148121   \n",
       "\n",
       "             Gas  \n",
       "Year              \n",
       "1980    2.487856  \n",
       "1981    2.883568  \n",
       "1982    4.360160  \n",
       "1983    5.243184  \n",
       "1984    6.170118  \n",
       "1985    7.199698  \n",
       "1986   10.310496  \n",
       "1987   12.776272  \n",
       "1988   15.205600  \n",
       "1989   18.517739  \n",
       "1990   21.097187  \n",
       "1991   23.563184  \n",
       "1992   27.256496  \n",
       "1993   26.996352  \n",
       "1994   28.996896  \n",
       "1995   39.050735  \n",
       "1996   39.746906  \n",
       "1997   46.023504  \n",
       "1998   47.595360  \n",
       "1999   42.315536  \n",
       "2000   43.872736  \n",
       "2001   43.568933  \n",
       "2002   47.133696  \n",
       "2003   49.987952  \n",
       "2004   49.584753  \n",
       "2005   50.438624  \n",
       "2006   49.442016  \n",
       "2007   52.695648  \n",
       "2008   63.046601  \n",
       "2009  112.684767  \n",
       "2010  134.244240  \n",
       "2011  136.364239  \n",
       "2012  125.604798  \n",
       "2013  110.689227  \n",
       "2014  109.467500  \n",
       "2015  106.870633  \n",
       "2016  113.622467  \n",
       "2017  118.922003  \n",
       "2018  126.201229  \n",
       "2019  128.675994  \n",
       "2020  127.635380  \n",
       "2021  133.450811  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm = pd.read_csv('Data/India.csv',  index_col='Year')\n",
    "dfm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67ec97d",
   "metadata": {},
   "source": [
    "## Data cleaning and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "615eb1f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>291.711364</td>\n",
       "      <td>197.337053</td>\n",
       "      <td>83.176464</td>\n",
       "      <td>2.487856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>314.966469</td>\n",
       "      <td>210.305446</td>\n",
       "      <td>89.713040</td>\n",
       "      <td>2.883568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>325.380295</td>\n",
       "      <td>213.123221</td>\n",
       "      <td>94.406624</td>\n",
       "      <td>4.360160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>352.199157</td>\n",
       "      <td>232.519456</td>\n",
       "      <td>99.169824</td>\n",
       "      <td>5.243184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>361.559743</td>\n",
       "      <td>231.492925</td>\n",
       "      <td>106.104774</td>\n",
       "      <td>6.170118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>397.592455</td>\n",
       "      <td>253.289252</td>\n",
       "      <td>116.759685</td>\n",
       "      <td>7.199698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>426.309754</td>\n",
       "      <td>273.171658</td>\n",
       "      <td>121.857312</td>\n",
       "      <td>10.310496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>455.343444</td>\n",
       "      <td>291.856287</td>\n",
       "      <td>127.671122</td>\n",
       "      <td>12.776272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>491.692596</td>\n",
       "      <td>317.172040</td>\n",
       "      <td>133.615088</td>\n",
       "      <td>15.205600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>540.651493</td>\n",
       "      <td>339.625896</td>\n",
       "      <td>150.714021</td>\n",
       "      <td>18.517739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>577.996538</td>\n",
       "      <td>366.514068</td>\n",
       "      <td>158.166611</td>\n",
       "      <td>21.097187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>615.365508</td>\n",
       "      <td>393.043111</td>\n",
       "      <td>166.422544</td>\n",
       "      <td>23.563184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>655.447568</td>\n",
       "      <td>411.415002</td>\n",
       "      <td>188.054800</td>\n",
       "      <td>27.256496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>677.299816</td>\n",
       "      <td>433.121813</td>\n",
       "      <td>186.878656</td>\n",
       "      <td>26.996352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>716.268589</td>\n",
       "      <td>455.720366</td>\n",
       "      <td>198.955200</td>\n",
       "      <td>28.996896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>761.449242</td>\n",
       "      <td>471.723186</td>\n",
       "      <td>216.281275</td>\n",
       "      <td>39.050735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>825.200806</td>\n",
       "      <td>504.498913</td>\n",
       "      <td>243.160345</td>\n",
       "      <td>39.746906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>858.935134</td>\n",
       "      <td>527.722798</td>\n",
       "      <td>244.201936</td>\n",
       "      <td>46.023504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>876.952083</td>\n",
       "      <td>524.125992</td>\n",
       "      <td>262.892000</td>\n",
       "      <td>47.595360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>950.950878</td>\n",
       "      <td>560.528326</td>\n",
       "      <td>300.510056</td>\n",
       "      <td>42.315536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>978.103843</td>\n",
       "      <td>572.876810</td>\n",
       "      <td>312.286549</td>\n",
       "      <td>43.872736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>991.731661</td>\n",
       "      <td>582.083456</td>\n",
       "      <td>316.115271</td>\n",
       "      <td>43.568933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1022.175223</td>\n",
       "      <td>598.598121</td>\n",
       "      <td>324.587833</td>\n",
       "      <td>47.133696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1058.725338</td>\n",
       "      <td>625.890755</td>\n",
       "      <td>329.936696</td>\n",
       "      <td>49.987952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1124.517704</td>\n",
       "      <td>669.968548</td>\n",
       "      <td>347.518127</td>\n",
       "      <td>49.584753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1184.926501</td>\n",
       "      <td>721.469674</td>\n",
       "      <td>351.646194</td>\n",
       "      <td>50.438624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1258.643267</td>\n",
       "      <td>773.565357</td>\n",
       "      <td>368.891165</td>\n",
       "      <td>49.442016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1356.953125</td>\n",
       "      <td>842.478182</td>\n",
       "      <td>391.027790</td>\n",
       "      <td>52.695648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1461.518268</td>\n",
       "      <td>910.796506</td>\n",
       "      <td>412.316303</td>\n",
       "      <td>63.046601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1611.416152</td>\n",
       "      <td>983.948220</td>\n",
       "      <td>431.609933</td>\n",
       "      <td>112.684767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1676.495006</td>\n",
       "      <td>1015.983089</td>\n",
       "      <td>438.373461</td>\n",
       "      <td>134.244240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1778.472509</td>\n",
       "      <td>1089.807084</td>\n",
       "      <td>458.909201</td>\n",
       "      <td>136.364239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1962.594350</td>\n",
       "      <td>1244.606043</td>\n",
       "      <td>488.554125</td>\n",
       "      <td>125.604798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2037.415492</td>\n",
       "      <td>1319.998650</td>\n",
       "      <td>495.642138</td>\n",
       "      <td>110.689227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2187.343005</td>\n",
       "      <td>1447.354894</td>\n",
       "      <td>511.166221</td>\n",
       "      <td>109.467500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2270.766191</td>\n",
       "      <td>1487.461399</td>\n",
       "      <td>554.788779</td>\n",
       "      <td>106.870633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2383.815964</td>\n",
       "      <td>1529.990408</td>\n",
       "      <td>613.165557</td>\n",
       "      <td>113.622467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2434.867906</td>\n",
       "      <td>1560.988150</td>\n",
       "      <td>631.087430</td>\n",
       "      <td>118.922003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2600.446463</td>\n",
       "      <td>1677.600364</td>\n",
       "      <td>655.190293</td>\n",
       "      <td>126.201229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2626.459473</td>\n",
       "      <td>1677.653708</td>\n",
       "      <td>674.011463</td>\n",
       "      <td>128.675994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2445.011942</td>\n",
       "      <td>1588.060207</td>\n",
       "      <td>603.602160</td>\n",
       "      <td>127.635380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2709.683625</td>\n",
       "      <td>1802.311897</td>\n",
       "      <td>622.148121</td>\n",
       "      <td>133.450811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total         Coal         Oil         Gas\n",
       "Year                                                  \n",
       "1980   291.711364   197.337053   83.176464    2.487856\n",
       "1981   314.966469   210.305446   89.713040    2.883568\n",
       "1982   325.380295   213.123221   94.406624    4.360160\n",
       "1983   352.199157   232.519456   99.169824    5.243184\n",
       "1984   361.559743   231.492925  106.104774    6.170118\n",
       "1985   397.592455   253.289252  116.759685    7.199698\n",
       "1986   426.309754   273.171658  121.857312   10.310496\n",
       "1987   455.343444   291.856287  127.671122   12.776272\n",
       "1988   491.692596   317.172040  133.615088   15.205600\n",
       "1989   540.651493   339.625896  150.714021   18.517739\n",
       "1990   577.996538   366.514068  158.166611   21.097187\n",
       "1991   615.365508   393.043111  166.422544   23.563184\n",
       "1992   655.447568   411.415002  188.054800   27.256496\n",
       "1993   677.299816   433.121813  186.878656   26.996352\n",
       "1994   716.268589   455.720366  198.955200   28.996896\n",
       "1995   761.449242   471.723186  216.281275   39.050735\n",
       "1996   825.200806   504.498913  243.160345   39.746906\n",
       "1997   858.935134   527.722798  244.201936   46.023504\n",
       "1998   876.952083   524.125992  262.892000   47.595360\n",
       "1999   950.950878   560.528326  300.510056   42.315536\n",
       "2000   978.103843   572.876810  312.286549   43.872736\n",
       "2001   991.731661   582.083456  316.115271   43.568933\n",
       "2002  1022.175223   598.598121  324.587833   47.133696\n",
       "2003  1058.725338   625.890755  329.936696   49.987952\n",
       "2004  1124.517704   669.968548  347.518127   49.584753\n",
       "2005  1184.926501   721.469674  351.646194   50.438624\n",
       "2006  1258.643267   773.565357  368.891165   49.442016\n",
       "2007  1356.953125   842.478182  391.027790   52.695648\n",
       "2008  1461.518268   910.796506  412.316303   63.046601\n",
       "2009  1611.416152   983.948220  431.609933  112.684767\n",
       "2010  1676.495006  1015.983089  438.373461  134.244240\n",
       "2011  1778.472509  1089.807084  458.909201  136.364239\n",
       "2012  1962.594350  1244.606043  488.554125  125.604798\n",
       "2013  2037.415492  1319.998650  495.642138  110.689227\n",
       "2014  2187.343005  1447.354894  511.166221  109.467500\n",
       "2015  2270.766191  1487.461399  554.788779  106.870633\n",
       "2016  2383.815964  1529.990408  613.165557  113.622467\n",
       "2017  2434.867906  1560.988150  631.087430  118.922003\n",
       "2018  2600.446463  1677.600364  655.190293  126.201229\n",
       "2019  2626.459473  1677.653708  674.011463  128.675994\n",
       "2020  2445.011942  1588.060207  603.602160  127.635380\n",
       "2021  2709.683625  1802.311897  622.148121  133.450811"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the categorial variables\n",
    "df= dfm.select_dtypes(include=['float64'])\n",
    "df"
   ]
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  {
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       "                            \n",
       "var gd = document.getElementById('21bdd9d4-3fa9-42b7-bc2e-5fc7df755e87');\n",
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       "\n",
       "// Listen for the removal of the full notebook cells\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  plot to see total emission versus year for 'Germany'\n",
    "fig_ger = px.line(df, x=df.index, y=\"Total\")\n",
    "fig_ger.update_xaxes(rangeslider_visible=True)\n",
    "fig_ger.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a651886",
   "metadata": {},
   "source": [
    "Visualize the time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d749f740",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, dpi=120, figsize=(10,6))\n",
    "for i, ax in enumerate(axes.flatten()):\n",
    "    data = df[df.columns[i]]\n",
    "    ax.plot(data, color='red', linewidth=1)\n",
    "    # Decorations\n",
    "    ax.set_title(df.columns[i])\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "    ax.spines[\"top\"].set_alpha(0)\n",
    "    ax.tick_params(labelsize=6)\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "56f8190d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb0582d5e80>"
      ]
     },
     "execution_count": 85,
     "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": [
    "plt.figure()\n",
    "\n",
    "x = df.index\n",
    "y1 = df['Total']\n",
    "y2 = df['Coal']\n",
    "y3 = df['Oil']\n",
    "y4 = df['Gas']\n",
    "\n",
    "plt.plot(x,y1)\n",
    "plt.plot(x,y2)\n",
    "plt.plot(x,y3)\n",
    "plt.plot(x,y4)\n",
    "\n",
    "plt.legend(['Total', 'Coal', 'Oil', 'Gas'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c08d95e3",
   "metadata": {},
   "source": [
    "## Preliminary tests that the multi-variable time series has to satisfy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95be9d06",
   "metadata": {},
   "source": [
    "### Grainger's Causality Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1f380907",
   "metadata": {
    "scrolled": false
   },
   "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>Total_x</th>\n",
       "      <th>Coal_x</th>\n",
       "      <th>Oil_x</th>\n",
       "      <th>Gas_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Total_y</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coal_y</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oil_y</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gas_y</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Total_x  Coal_x  Oil_x  Gas_x\n",
       "Total_y      1.0     0.0    0.0    0.0\n",
       "Coal_y       0.0     1.0    0.0    0.0\n",
       "Oil_y        0.0     0.0    1.0    0.0\n",
       "Gas_y        0.0     0.0    0.0    1.0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "maxlag=12\n",
    "test = 'ssr_chi2test'\n",
    "def grangers_causation_matrix(data, variables, test='ssr_chi2test', verbose=False):    \n",
    "    \"\"\"Check Granger Causality of all possible combinations of the Time series.\n",
    "    The rows are the response variable, columns are predictors. The values in the table \n",
    "    are the P-Values. P-Values lesser than the significance level (0.05), implies \n",
    "    the Null Hypothesis that the coefficients of the corresponding past values is \n",
    "    zero, that is, the X does not cause Y can be rejected.\n",
    "\n",
    "    data      : pandas dataframe containing the time series variables\n",
    "    variables : list containing names of the time series variables.\n",
    "    \"\"\"\n",
    "    df = pd.DataFrame(np.zeros((len(variables), len(variables))), columns=variables, index=variables)\n",
    "    for c in df.columns:\n",
    "        for r in df.index:\n",
    "            test_result = grangercausalitytests(data[[r, c]], maxlag=maxlag, verbose=False)\n",
    "            p_values = [round(test_result[i+1][0][test][1],4) for i in range(maxlag)]\n",
    "            if verbose: print(f'Y = {r}, X = {c}, P Values = {p_values}')\n",
    "            min_p_value = np.min(p_values)\n",
    "            df.loc[r, c] = min_p_value\n",
    "    df.columns = [var + '_x' for var in variables]\n",
    "    df.index = [var + '_y' for var in variables]\n",
    "    return df\n",
    "\n",
    "grangers_causation_matrix(df, variables = df.columns) # the non-diagonal elements should have values less that 0.05 (significance level)   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a19284f",
   "metadata": {},
   "source": [
    "### Cointegration Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8a7778a1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name   ::  Test Stat > C(95%)    =>   Signif  \n",
      " ----------------------------------------\n",
      "Total  ::  109.7     > 40.1749   =>   True\n",
      "Coal   ::  57.59     > 24.2761   =>   True\n",
      "Oil    ::  22.37     > 12.3212   =>   True\n",
      "Gas    ::  3.49      > 4.1296    =>   False\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen\n",
    "\n",
    "def cointegration_test(df, alpha=0.05): \n",
    "    \"\"\"Perform Johanson's Cointegration Test and Report Summary\"\"\"\n",
    "    out = coint_johansen(df,-1,5)\n",
    "    d = {'0.90':0, '0.95':1, '0.99':2}\n",
    "    traces = out.lr1\n",
    "    cvts = out.cvt[:, d[str(1-alpha)]]\n",
    "    def adjust(val, length= 6): return str(val).ljust(length)\n",
    "\n",
    "    # Summary\n",
    "    print('Name   ::  Test Stat > C(95%)    =>   Signif  \\n', '--'*20)\n",
    "    for col, trace, cvt in zip(df.columns, traces, cvts):\n",
    "        print(adjust(col), ':: ', adjust(round(trace,2), 9), \">\", adjust(cvt, 8), ' =>  ' , trace > cvt)\n",
    "\n",
    "cointegration_test(df) # ideal case is when Signif is True for all the time series"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b21b3948",
   "metadata": {},
   "source": [
    "## Test-train split and the Augmented Dickey Fuller test for staionarity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6192c1ea",
   "metadata": {},
   "source": [
    "### Split the Series into Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b953b6ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37, 4)\n",
      "(5, 4)\n"
     ]
    }
   ],
   "source": [
    "nobs = 5\n",
    "df_train, df_test = df[0:-nobs], df[-nobs:]\n",
    "\n",
    "# Check size\n",
    "print(df_train.shape)  \n",
    "print(df_test.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a33b9eec",
   "metadata": {},
   "source": [
    "###  Check for Stationarity and Make the Time Series Stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fcb28513",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adfuller_test(series, signif=0.05, name='', verbose=False):\n",
    "    \"\"\"Perform ADFuller to test for Stationarity of given series and print report\"\"\"\n",
    "    r = adfuller(series, autolag='AIC')\n",
    "    output = {'test_statistic':round(r[0], 4), 'pvalue':round(r[1], 4), 'n_lags':round(r[2], 4), 'n_obs':r[3]}\n",
    "    p_value = output['pvalue'] \n",
    "    def adjust(val, length= 6): return str(val).ljust(length)\n",
    "\n",
    "    # Print Summary\n",
    "    print(f'    Augmented Dickey-Fuller Test on \"{name}\"', \"\\n   \", '-'*47)\n",
    "    print(f' Null Hypothesis: Data has unit root. Non-Stationary.')\n",
    "    print(f' Significance Level    = {signif}')\n",
    "    print(f' Test Statistic        = {output[\"test_statistic\"]}')\n",
    "    print(f' No. Lags Chosen       = {output[\"n_lags\"]}')\n",
    "\n",
    "    for key,val in r[4].items():\n",
    "        print(f' Critical value {adjust(key)} = {round(val, 3)}')\n",
    "\n",
    "    if p_value <= signif:\n",
    "        print(f\" => P-Value = {p_value}. Rejecting Null Hypothesis.\")\n",
    "        print(f\" => Series is Stationary.\")\n",
    "    else:\n",
    "        print(f\" => P-Value = {p_value}. Weak evidence to reject the Null Hypothesis.\")\n",
    "        print(f\" => Series is Non-Stationary.\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "7d2de168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"Total\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 2.1619\n",
      " No. Lags Chosen       = 6\n",
      " Critical value 1%     = -3.67\n",
      " Critical value 5%     = -2.964\n",
      " Critical value 10%    = -2.621\n",
      " => P-Value = 0.9988. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Coal\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 5.4664\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.627\n",
      " Critical value 5%     = -2.946\n",
      " Critical value 10%    = -2.612\n",
      " => P-Value = 1.0. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Oil\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 3.7201\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.627\n",
      " Critical value 5%     = -2.946\n",
      " Critical value 10%    = -2.612\n",
      " => P-Value = 1.0. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Gas\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 1.7302\n",
      " No. Lags Chosen       = 10\n",
      " Critical value 1%     = -3.711\n",
      " Critical value 5%     = -2.981\n",
      " Critical value 10%    = -2.63\n",
      " => P-Value = 0.9982. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ADF Test on each column\n",
    "for name, column in df_train.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "73ae4f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The ADF test confirms none of the time series is stationary. Let’s difference all of them once and check again.\n",
    "# 1st difference\n",
    "df_differenced = df_train.diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3b06cde5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"Total\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 0.0039\n",
      " No. Lags Chosen       = 10\n",
      " Critical value 1%     = -3.724\n",
      " Critical value 5%     = -2.986\n",
      " Critical value 10%    = -2.633\n",
      " => P-Value = 0.9589. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Coal\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = 0.3814\n",
      " No. Lags Chosen       = 4\n",
      " Critical value 1%     = -3.661\n",
      " Critical value 5%     = -2.961\n",
      " Critical value 10%    = -2.619\n",
      " => P-Value = 0.9808. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Oil\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -2.7659\n",
      " No. Lags Chosen       = 0\n",
      " Critical value 1%     = -3.633\n",
      " Critical value 5%     = -2.949\n",
      " Critical value 10%    = -2.613\n",
      " => P-Value = 0.0633. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Gas\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -4.1578\n",
      " No. Lags Chosen       = 9\n",
      " Critical value 1%     = -3.711\n",
      " Critical value 5%     = -2.981\n",
      " Critical value 10%    = -2.63\n",
      " => P-Value = 0.0008. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Re-run ADF test on each differenced series.\n",
    "# ADF Test on each column of 1st Differences Dataframe\n",
    "for name, column in df_differenced.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d3a7b0d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let’s difference all of them once more and check again.\n",
    "# 2nd difference\n",
    "df_differenced = df_differenced.diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fb863e02",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Augmented Dickey-Fuller Test on \"Total\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -2.3157\n",
      " No. Lags Chosen       = 7\n",
      " Critical value 1%     = -3.7\n",
      " Critical value 5%     = -2.976\n",
      " Critical value 10%    = -2.628\n",
      " => P-Value = 0.1669. Weak evidence to reject the Null Hypothesis.\n",
      " => Series is Non-Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Coal\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -4.6802\n",
      " No. Lags Chosen       = 3\n",
      " Critical value 1%     = -3.661\n",
      " Critical value 5%     = -2.961\n",
      " Critical value 10%    = -2.619\n",
      " => P-Value = 0.0001. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Oil\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -5.0432\n",
      " No. Lags Chosen       = 2\n",
      " Critical value 1%     = -3.654\n",
      " Critical value 5%     = -2.957\n",
      " Critical value 10%    = -2.618\n",
      " => P-Value = 0.0. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n",
      "    Augmented Dickey-Fuller Test on \"Gas\" \n",
      "    -----------------------------------------------\n",
      " Null Hypothesis: Data has unit root. Non-Stationary.\n",
      " Significance Level    = 0.05\n",
      " Test Statistic        = -4.1701\n",
      " No. Lags Chosen       = 10\n",
      " Critical value 1%     = -3.738\n",
      " Critical value 5%     = -2.992\n",
      " Critical value 10%    = -2.636\n",
      " => P-Value = 0.0007. Rejecting Null Hypothesis.\n",
      " => Series is Stationary.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Re-run ADF test on each differenced series.\n",
    "# ADF Test on each column of 2nd Differences Dataframe\n",
    "for name, column in df_differenced.iteritems():\n",
    "    adfuller_test(column, name=column.name)\n",
    "    print('\\n')\n",
    "    # after second differencing, all the times series except 'total' is non-stationary. But we continue with the forecasting with some risks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342746e9",
   "metadata": {},
   "source": [
    "## Vector autoregression (VAR) modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b25797e6",
   "metadata": {},
   "source": [
    "### Selecting the order/lag (p) of VAR model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e95a347e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag Order = 1\n",
      "AIC :  18.288882012156925\n",
      "BIC :  19.18674114428408\n",
      "FPE :  88408147.39779209\n",
      "HQIC:  18.595077690073676 \n",
      "\n",
      "Lag Order = 2\n",
      "AIC :  18.593584322650628\n",
      "BIC :  20.226138026068604\n",
      "FPE :  125803194.14576498\n",
      "HQIC:  19.142888971540128 \n",
      "\n",
      "Lag Order = 3\n",
      "AIC :  17.8806923770314\n",
      "BIC :  20.262513219080958\n",
      "FPE :  71099184.10896353\n",
      "HQIC:  18.67019860055182 \n",
      "\n",
      "Lag Order = 4\n",
      "AIC :  16.621823647450466\n",
      "BIC :  19.767343966966273\n",
      "FPE :  28439841.746860385\n",
      "HQIC:  17.647184839148238 \n",
      "\n",
      "Lag Order = 5\n",
      "AIC :  16.033700404110704\n",
      "BIC :  19.957053072764737\n",
      "FPE :  35043637.397919156\n",
      "HQIC:  17.28881463203934 \n",
      "\n",
      "Lag Order = 6\n",
      "AIC :  10.234568993165004\n",
      "BIC :  14.949382200014915\n",
      "FPE :  935443.484511517\n",
      "HQIC:  11.7111896684283 \n",
      "\n",
      "Lag Order = 7\n",
      "AIC :  -225.7810087239123\n",
      "BIC :  -220.26187575318644\n",
      "FPE :  2.3421420020375964e-95\n",
      "HQIC:  -224.0937547522793 \n",
      "\n",
      "Lag Order = 8\n",
      "AIC :  -232.15598081488884\n",
      "BIC :  -225.82077835886767\n",
      "FPE :  8.501658522035268e-102\n",
      "HQIC:  -230.27219301121514 \n",
      "\n",
      "Lag Order = 9\n",
      "AIC :  -235.10858646234263\n",
      "BIC :  -227.94711386129728\n",
      "FPE :  9.575337014550914e-105\n",
      "HQIC:  -233.04634147466572 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# To select the right order of the VAR model, we iteratively fit increasing orders of VAR model \n",
    "# and pick the order that gives a model with least AIC and BIC.\n",
    "\n",
    "model = VAR(df_differenced)\n",
    "for i in [1,2,3,4,5,6,7,8,9]:\n",
    "    result = model.fit(i)\n",
    "    print('Lag Order =', i)\n",
    "    print('AIC : ', result.aic)\n",
    "    print('BIC : ', result.bic)\n",
    "    print('FPE : ', result.fpe)\n",
    "    print('HQIC: ', result.hqic, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "933e118c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# An alternate method to choose the order(p) of the VAR models is to use the model.select_order(maxlags) method.\n",
    "#x = model.select_order(maxlags=6)\n",
    "#x.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810cce81",
   "metadata": {},
   "source": [
    "### Train theVAR model for the  selected order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "42f69ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Thu, 09, Mar, 2023\n",
       "Time:                     14:47:59\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         4.00000    BIC:                    19.1867\n",
       "Nobs:                     34.0000    HQIC:                   18.5951\n",
       "Log likelihood:          -483.887    FPE:                8.84081e+07\n",
       "AIC:                      18.2889    Det(Omega_mle):     5.10681e+07\n",
       "--------------------------------------------------------------------\n",
       "Results for equation Total\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            3.469405         5.207863            0.666           0.505\n",
       "L1.Total        -0.765385         2.545407           -0.301           0.764\n",
       "L1.Coal          0.090118         2.691261            0.033           0.973\n",
       "L1.Oil           0.705485         2.593444            0.272           0.786\n",
       "L1.Gas          -0.707967         2.645570           -0.268           0.789\n",
       "===========================================================================\n",
       "\n",
       "Results for equation Coal\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            1.420959         4.210458            0.337           0.736\n",
       "L1.Total         0.140014         2.057913            0.068           0.946\n",
       "L1.Coal         -0.663332         2.175833           -0.305           0.760\n",
       "L1.Oil          -0.092066         2.096750           -0.044           0.965\n",
       "L1.Gas          -1.467020         2.138893           -0.686           0.493\n",
       "===========================================================================\n",
       "\n",
       "Results for equation Oil\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            1.826808         2.239378            0.816           0.415\n",
       "L1.Total        -0.770826         1.094523           -0.704           0.481\n",
       "L1.Coal          0.691169         1.157241            0.597           0.550\n",
       "L1.Oil           0.578035         1.115179            0.518           0.604\n",
       "L1.Gas           0.749590         1.137594            0.659           0.510\n",
       "===========================================================================\n",
       "\n",
       "Results for equation Gas\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            0.130353         1.891017            0.069           0.945\n",
       "L1.Total         0.242497         0.924258            0.262           0.793\n",
       "L1.Coal         -0.303269         0.977219           -0.310           0.756\n",
       "L1.Oil          -0.173341         0.941700           -0.184           0.854\n",
       "L1.Gas          -0.304528         0.960628           -0.317           0.751\n",
       "===========================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "            Total      Coal       Oil       Gas\n",
       "Total    1.000000  0.835266  0.451055  0.235265\n",
       "Coal     0.835266  1.000000  0.060900 -0.117226\n",
       "Oil      0.451055  0.060900  1.000000 -0.089250\n",
       "Gas      0.235265 -0.117226 -0.089250  1.000000\n",
       "\n"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=1 # We choose p = 1 as the number of lags (AIC and BIC is locally minimum)\n",
    "model_fitted = model.fit(p)\n",
    "model_fitted.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30f305b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8c58acb4",
   "metadata": {},
   "source": [
    "### Serial Correlation of Residuals (Errors) using Durbin Watson Statistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d65387cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total  : 2.56\n",
      "Coal   : 2.32\n",
      "Oil    : 2.37\n",
      "Gas    : 1.98\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.stattools import durbin_watson\n",
    "\n",
    "def adjust(val, length= 6): return str(val).ljust(length)\n",
    "out = durbin_watson(model_fitted.resid)\n",
    "\n",
    "for col, val in zip(df.columns, out):\n",
    "    print(adjust(col), ':', round(val, 2))\n",
    "    # Results close to 2 is alright."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda4d416",
   "metadata": {},
   "source": [
    "### Testing the VAR model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "5d722d89",
   "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>Total</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>-12.841279</td>\n",
       "      <td>-10.150618</td>\n",
       "      <td>-1.842992</td>\n",
       "      <td>1.080880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>16.405036</td>\n",
       "      <td>16.578460</td>\n",
       "      <td>0.069616</td>\n",
       "      <td>-0.593568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>-17.458276</td>\n",
       "      <td>-20.422766</td>\n",
       "      <td>2.171750</td>\n",
       "      <td>0.043910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>26.672126</td>\n",
       "      <td>22.822858</td>\n",
       "      <td>3.719961</td>\n",
       "      <td>0.102646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>-7.315413</td>\n",
       "      <td>-1.913921</td>\n",
       "      <td>-5.557284</td>\n",
       "      <td>2.081218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>0.316391</td>\n",
       "      <td>-1.197777</td>\n",
       "      <td>0.716183</td>\n",
       "      <td>-0.645022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>7.315462</td>\n",
       "      <td>6.631124</td>\n",
       "      <td>0.130156</td>\n",
       "      <td>-0.036448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>12.609745</td>\n",
       "      <td>-2.861897</td>\n",
       "      <td>11.154967</td>\n",
       "      <td>0.882811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>-11.613852</td>\n",
       "      <td>4.434316</td>\n",
       "      <td>-9.646343</td>\n",
       "      <td>-0.732691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>0.023925</td>\n",
       "      <td>-0.359129</td>\n",
       "      <td>0.803343</td>\n",
       "      <td>-0.113451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>2.713090</td>\n",
       "      <td>-8.157152</td>\n",
       "      <td>13.376323</td>\n",
       "      <td>1.227315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>-18.229812</td>\n",
       "      <td>3.334920</td>\n",
       "      <td>-22.808400</td>\n",
       "      <td>-3.953456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>17.116525</td>\n",
       "      <td>0.891742</td>\n",
       "      <td>13.252688</td>\n",
       "      <td>2.260688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>6.211880</td>\n",
       "      <td>-6.595733</td>\n",
       "      <td>5.249531</td>\n",
       "      <td>8.053295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>18.570911</td>\n",
       "      <td>16.772907</td>\n",
       "      <td>9.552995</td>\n",
       "      <td>-9.357668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-30.017236</td>\n",
       "      <td>-9.551842</td>\n",
       "      <td>-25.837479</td>\n",
       "      <td>5.580427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-15.717379</td>\n",
       "      <td>-26.820691</td>\n",
       "      <td>17.648473</td>\n",
       "      <td>-4.704742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>55.981846</td>\n",
       "      <td>39.999140</td>\n",
       "      <td>18.927992</td>\n",
       "      <td>-6.851680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>-46.845830</td>\n",
       "      <td>-24.053850</td>\n",
       "      <td>-25.841563</td>\n",
       "      <td>6.837024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-13.525147</td>\n",
       "      <td>-3.141838</td>\n",
       "      <td>-7.947771</td>\n",
       "      <td>-1.861003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>16.815744</td>\n",
       "      <td>7.308019</td>\n",
       "      <td>4.643840</td>\n",
       "      <td>3.868566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>6.106553</td>\n",
       "      <td>10.777969</td>\n",
       "      <td>-3.123699</td>\n",
       "      <td>-0.710507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>29.242251</td>\n",
       "      <td>16.785159</td>\n",
       "      <td>12.232568</td>\n",
       "      <td>-3.257455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>-5.383569</td>\n",
       "      <td>7.423333</td>\n",
       "      <td>-13.453364</td>\n",
       "      <td>1.257070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>13.307969</td>\n",
       "      <td>0.594557</td>\n",
       "      <td>13.116904</td>\n",
       "      <td>-1.850479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>24.593092</td>\n",
       "      <td>16.817142</td>\n",
       "      <td>4.891654</td>\n",
       "      <td>4.250240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>6.255285</td>\n",
       "      <td>-0.594501</td>\n",
       "      <td>-0.848112</td>\n",
       "      <td>7.097321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>45.332741</td>\n",
       "      <td>4.833390</td>\n",
       "      <td>-1.994883</td>\n",
       "      <td>39.287213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>-84.819030</td>\n",
       "      <td>-41.116845</td>\n",
       "      <td>-12.530102</td>\n",
       "      <td>-28.078693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>36.898649</td>\n",
       "      <td>41.789126</td>\n",
       "      <td>13.772212</td>\n",
       "      <td>-19.439474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>82.144338</td>\n",
       "      <td>80.974964</td>\n",
       "      <td>9.109184</td>\n",
       "      <td>-12.879440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>-109.300699</td>\n",
       "      <td>-79.406352</td>\n",
       "      <td>-22.556911</td>\n",
       "      <td>-4.156130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>75.106371</td>\n",
       "      <td>51.963637</td>\n",
       "      <td>8.436070</td>\n",
       "      <td>13.693844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>-66.504327</td>\n",
       "      <td>-87.249739</td>\n",
       "      <td>28.098475</td>\n",
       "      <td>-1.375140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>29.626587</td>\n",
       "      <td>2.422504</td>\n",
       "      <td>14.754220</td>\n",
       "      <td>9.348701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total       Coal        Oil        Gas\n",
       "Year                                             \n",
       "1982  -12.841279 -10.150618  -1.842992   1.080880\n",
       "1983   16.405036  16.578460   0.069616  -0.593568\n",
       "1984  -17.458276 -20.422766   2.171750   0.043910\n",
       "1985   26.672126  22.822858   3.719961   0.102646\n",
       "1986   -7.315413  -1.913921  -5.557284   2.081218\n",
       "1987    0.316391  -1.197777   0.716183  -0.645022\n",
       "1988    7.315462   6.631124   0.130156  -0.036448\n",
       "1989   12.609745  -2.861897  11.154967   0.882811\n",
       "1990  -11.613852   4.434316  -9.646343  -0.732691\n",
       "1991    0.023925  -0.359129   0.803343  -0.113451\n",
       "1992    2.713090  -8.157152  13.376323   1.227315\n",
       "1993  -18.229812   3.334920 -22.808400  -3.953456\n",
       "1994   17.116525   0.891742  13.252688   2.260688\n",
       "1995    6.211880  -6.595733   5.249531   8.053295\n",
       "1996   18.570911  16.772907   9.552995  -9.357668\n",
       "1997  -30.017236  -9.551842 -25.837479   5.580427\n",
       "1998  -15.717379 -26.820691  17.648473  -4.704742\n",
       "1999   55.981846  39.999140  18.927992  -6.851680\n",
       "2000  -46.845830 -24.053850 -25.841563   6.837024\n",
       "2001  -13.525147  -3.141838  -7.947771  -1.861003\n",
       "2002   16.815744   7.308019   4.643840   3.868566\n",
       "2003    6.106553  10.777969  -3.123699  -0.710507\n",
       "2004   29.242251  16.785159  12.232568  -3.257455\n",
       "2005   -5.383569   7.423333 -13.453364   1.257070\n",
       "2006   13.307969   0.594557  13.116904  -1.850479\n",
       "2007   24.593092  16.817142   4.891654   4.250240\n",
       "2008    6.255285  -0.594501  -0.848112   7.097321\n",
       "2009   45.332741   4.833390  -1.994883  39.287213\n",
       "2010  -84.819030 -41.116845 -12.530102 -28.078693\n",
       "2011   36.898649  41.789126  13.772212 -19.439474\n",
       "2012   82.144338  80.974964   9.109184 -12.879440\n",
       "2013 -109.300699 -79.406352 -22.556911  -4.156130\n",
       "2014   75.106371  51.963637   8.436070  13.693844\n",
       "2015  -66.504327 -87.249739  28.098475  -1.375140\n",
       "2016   29.626587   2.422504  14.754220   9.348701"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_differenced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "a67dd70d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[29.626587,  2.422504, 14.75422 ,  9.348701]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to forecast, the VAR model expects up to the lag number (p) of observations from the past data.\n",
    "# Get the lag order\n",
    "lag_order = model_fitted.k_ar\n",
    "print(lag_order)  \n",
    "\n",
    "# Input data for forecasting\n",
    "forecast_input = df_differenced.values[-lag_order:]\n",
    "forecast_input # the output is the values of total, coal, oil and gas for 2015 and 2016 (this depends on the train test split and lag_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "84380c81",
   "metadata": {
    "scrolled": true
   },
   "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>Total_2d</th>\n",
       "      <th>Coal_2d</th>\n",
       "      <th>Oil_2d</th>\n",
       "      <th>Gas_2d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-15.197715</td>\n",
       "      <td>-11.110911</td>\n",
       "      <td>-3.799619</td>\n",
       "      <td>1.175596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>10.587349</td>\n",
       "      <td>5.288481</td>\n",
       "      <td>4.546979</td>\n",
       "      <td>0.115177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-1.031117</td>\n",
       "      <td>-1.192270</td>\n",
       "      <td>0.035692</td>\n",
       "      <td>0.270669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>3.984716</td>\n",
       "      <td>1.667096</td>\n",
       "      <td>2.021081</td>\n",
       "      <td>0.153276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1.887129</td>\n",
       "      <td>0.462106</td>\n",
       "      <td>1.190682</td>\n",
       "      <td>0.194043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total_2d    Coal_2d    Oil_2d    Gas_2d\n",
       "Year                                          \n",
       "2017 -15.197715 -11.110911 -3.799619  1.175596\n",
       "2018  10.587349   5.288481  4.546979  0.115177\n",
       "2019  -1.031117  -1.192270  0.035692  0.270669\n",
       "2020   3.984716   1.667096  2.021081  0.153276\n",
       "2021   1.887129   0.462106  1.190682  0.194043"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Forecast\n",
    "fc = model_fitted.forecast(y=forecast_input, steps=nobs) # nobs is from test train split ratio\n",
    "df_forecast = pd.DataFrame(fc, index=df.index[-nobs:], columns=df.columns + '_2d')\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "55f36737",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Invert the transformation to get the real forecast\n",
    "def invert_transformation(df_train, df_forecast, second_diff=False):\n",
    "    \"\"\"Revert back the differencing to get the forecast to original scale.\"\"\"\n",
    "    df_fc = df_forecast.copy()\n",
    "    columns = df_train.columns\n",
    "    for col in columns:        \n",
    "        # Roll back 2nd Diff\n",
    "        if second_diff:\n",
    "            df_fc[str(col)+'_1d'] = (df_train[col].iloc[-1]-df_train[col].iloc[-2]) + df_fc[str(col)+'_2d'].cumsum()\n",
    "        # Roll back 1st Diff\n",
    "        df_fc[str(col)+'_forecast'] = df_train[col].iloc[-1] + df_fc[str(col)+'_1d'].cumsum()\n",
    "    return df_fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "64c9e178",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_forecast</th>\n",
       "      <th>Coal_forecast</th>\n",
       "      <th>Oil_forecast</th>\n",
       "      <th>Gas_forecast</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2481.668022</td>\n",
       "      <td>1561.408506</td>\n",
       "      <td>667.742716</td>\n",
       "      <td>121.549897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2590.107430</td>\n",
       "      <td>1598.115085</td>\n",
       "      <td>726.866855</td>\n",
       "      <td>129.592503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2697.515721</td>\n",
       "      <td>1633.629394</td>\n",
       "      <td>786.026685</td>\n",
       "      <td>137.905779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2808.908728</td>\n",
       "      <td>1670.810799</td>\n",
       "      <td>847.207596</td>\n",
       "      <td>146.372330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2922.188864</td>\n",
       "      <td>1708.454311</td>\n",
       "      <td>909.579190</td>\n",
       "      <td>155.032925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total_forecast  Coal_forecast  Oil_forecast  Gas_forecast\n",
       "Year                                                           \n",
       "2017     2481.668022    1561.408506    667.742716    121.549897\n",
       "2018     2590.107430    1598.115085    726.866855    129.592503\n",
       "2019     2697.515721    1633.629394    786.026685    137.905779\n",
       "2020     2808.908728    1670.810799    847.207596    146.372330\n",
       "2021     2922.188864    1708.454311    909.579190    155.032925"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results = invert_transformation(df_train, df_forecast, second_diff=True)        \n",
    "df_results.loc[:, ['Total_forecast', 'Coal_forecast', 'Oil_forecast','Gas_forecast']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "d4436fb8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    }\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>Total</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2434.867906</td>\n",
       "      <td>1560.988150</td>\n",
       "      <td>631.087430</td>\n",
       "      <td>118.922003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2600.446463</td>\n",
       "      <td>1677.600364</td>\n",
       "      <td>655.190293</td>\n",
       "      <td>126.201229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2626.459473</td>\n",
       "      <td>1677.653708</td>\n",
       "      <td>674.011463</td>\n",
       "      <td>128.675994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2445.011942</td>\n",
       "      <td>1588.060207</td>\n",
       "      <td>603.602160</td>\n",
       "      <td>127.635380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2709.683625</td>\n",
       "      <td>1802.311897</td>\n",
       "      <td>622.148121</td>\n",
       "      <td>133.450811</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total         Coal         Oil         Gas\n",
       "Year                                                  \n",
       "2017  2434.867906  1560.988150  631.087430  118.922003\n",
       "2018  2600.446463  1677.600364  655.190293  126.201229\n",
       "2019  2626.459473  1677.653708  674.011463  128.675994\n",
       "2020  2445.011942  1588.060207  603.602160  127.635380\n",
       "2021  2709.683625  1802.311897  622.148121  133.450811"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44d5f38d",
   "metadata": {},
   "source": [
    "### Plot of Forecast vs Actuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "1e3ab499",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=int(len(df.columns)/2), ncols=2, dpi=150, figsize=(10,10))\n",
    "for i, (col,ax) in enumerate(zip(df.columns, axes.flatten())):\n",
    "    df_results[col+'_forecast'].plot(legend=True, ax=ax).autoscale(axis='x',tight=True)\n",
    "    df_test[col][-nobs:].plot(legend=True, ax=ax);\n",
    "    ax.set_title(col + \": Forecast vs Actuals\")\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "    ax.spines[\"top\"].set_alpha(0)\n",
    "    ax.tick_params(labelsize=6)\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "385d1c19",
   "metadata": {},
   "source": [
    "Evaluate the Forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "929c7a6c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecast Accuracy of: Total\n",
      "mape   :  0.0555\n",
      "me     :  136.7839\n",
      "mae    :  140.9195\n",
      "mpe    :  0.0539\n",
      "rmse   :  192.3151\n",
      "corr   :  0.5219\n",
      "minmax :  0.0503\n",
      "\n",
      "Forecast Accuracy of: Coal\n",
      "mape   :  0.0356\n",
      "me     :  -26.8392\n",
      "mae    :  60.1076\n",
      "mpe    :  -0.0147\n",
      "rmse   :  69.1565\n",
      "corr   :  0.66\n",
      "minmax :  0.0351\n",
      "\n",
      "Forecast Accuracy of: Oil\n",
      "mape   :  0.2399\n",
      "me     :  150.2767\n",
      "mae    :  150.2767\n",
      "mpe    :  0.2399\n",
      "rmse   :  179.4373\n",
      "corr   :  -0.4021\n",
      "minmax :  0.1799\n",
      "\n",
      "Forecast Accuracy of: Gas\n",
      "mape   :  0.0858\n",
      "me     :  11.1136\n",
      "mae    :  11.1136\n",
      "mpe    :  0.0858\n",
      "rmse   :  13.568\n",
      "corr   :  0.9138\n",
      "minmax :  0.0764\n"
     ]
    }
   ],
   "source": [
    "# To evaluate the forecasts, let’s compute a comprehensive set of metrics, namely, the MAPE, ME, MAE, MPE, RMSE, corr and minmax.\n",
    "from statsmodels.tsa.stattools import acf\n",
    "def forecast_accuracy(forecast, actual):\n",
    "    mape = np.mean(np.abs(forecast - actual)/np.abs(actual))  # MAPE\n",
    "    me = np.mean(forecast - actual)             # ME\n",
    "    mae = np.mean(np.abs(forecast - actual))    # MAE\n",
    "    mpe = np.mean((forecast - actual)/actual)   # MPE\n",
    "    rmse = np.mean((forecast - actual)**2)**.5  # RMSE\n",
    "    corr = np.corrcoef(forecast, actual)[0,1]   # corr\n",
    "    mins = np.amin(np.hstack([forecast[:,None], \n",
    "                              actual[:,None]]), axis=1)\n",
    "    maxs = np.amax(np.hstack([forecast[:,None], \n",
    "                              actual[:,None]]), axis=1)\n",
    "    minmax = 1 - np.mean(mins/maxs)             # minmax\n",
    "    return({'mape':mape, 'me':me, 'mae': mae, \n",
    "            'mpe': mpe, 'rmse':rmse, 'corr':corr, 'minmax':minmax})\n",
    "\n",
    "print('Forecast Accuracy of: Total')\n",
    "accuracy_prod = forecast_accuracy(df_results['Total_forecast'].values, df_test['Total'])\n",
    "for k, v in accuracy_prod.items():\n",
    "    print(adjust(k), ': ', round(v,4))\n",
    "\n",
    "print('\\nForecast Accuracy of: Coal')\n",
    "accuracy_prod = forecast_accuracy(df_results['Coal_forecast'].values, df_test['Coal'])\n",
    "for k, v in accuracy_prod.items():\n",
    "    print(adjust(k), ': ', round(v,4))\n",
    "\n",
    "print('\\nForecast Accuracy of: Oil')\n",
    "accuracy_prod = forecast_accuracy(df_results['Oil_forecast'].values, df_test['Oil'])\n",
    "for k, v in accuracy_prod.items():\n",
    "    print(adjust(k), ': ', round(v,4))\n",
    "\n",
    "print('\\nForecast Accuracy of: Gas')\n",
    "accuracy_prod = forecast_accuracy(df_results['Gas_forecast'].values, df_test['Gas'])\n",
    "for k, v in accuracy_prod.items():\n",
    "    print(adjust(k), ': ', round(v,4))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70eb5b6",
   "metadata": {},
   "source": [
    "## Forecasting the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b02b3e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_differenced_all = df.diff().dropna() # for forecasting the future, one requires the whole df, i.e., until 2021\n",
    "df_differenced_all = df_differenced_all.diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "aa5beec3",
   "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>Total</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>-12.841279</td>\n",
       "      <td>-10.150618</td>\n",
       "      <td>-1.842992</td>\n",
       "      <td>1.080880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>16.405036</td>\n",
       "      <td>16.578460</td>\n",
       "      <td>0.069616</td>\n",
       "      <td>-0.593568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>-17.458276</td>\n",
       "      <td>-20.422766</td>\n",
       "      <td>2.171750</td>\n",
       "      <td>0.043910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>26.672126</td>\n",
       "      <td>22.822858</td>\n",
       "      <td>3.719961</td>\n",
       "      <td>0.102646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>-7.315413</td>\n",
       "      <td>-1.913921</td>\n",
       "      <td>-5.557284</td>\n",
       "      <td>2.081218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>0.316391</td>\n",
       "      <td>-1.197777</td>\n",
       "      <td>0.716183</td>\n",
       "      <td>-0.645022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>7.315462</td>\n",
       "      <td>6.631124</td>\n",
       "      <td>0.130156</td>\n",
       "      <td>-0.036448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>12.609745</td>\n",
       "      <td>-2.861897</td>\n",
       "      <td>11.154967</td>\n",
       "      <td>0.882811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>-11.613852</td>\n",
       "      <td>4.434316</td>\n",
       "      <td>-9.646343</td>\n",
       "      <td>-0.732691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>0.023925</td>\n",
       "      <td>-0.359129</td>\n",
       "      <td>0.803343</td>\n",
       "      <td>-0.113451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>2.713090</td>\n",
       "      <td>-8.157152</td>\n",
       "      <td>13.376323</td>\n",
       "      <td>1.227315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>-18.229812</td>\n",
       "      <td>3.334920</td>\n",
       "      <td>-22.808400</td>\n",
       "      <td>-3.953456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>17.116525</td>\n",
       "      <td>0.891742</td>\n",
       "      <td>13.252688</td>\n",
       "      <td>2.260688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>6.211880</td>\n",
       "      <td>-6.595733</td>\n",
       "      <td>5.249531</td>\n",
       "      <td>8.053295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>18.570911</td>\n",
       "      <td>16.772907</td>\n",
       "      <td>9.552995</td>\n",
       "      <td>-9.357668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-30.017236</td>\n",
       "      <td>-9.551842</td>\n",
       "      <td>-25.837479</td>\n",
       "      <td>5.580427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-15.717379</td>\n",
       "      <td>-26.820691</td>\n",
       "      <td>17.648473</td>\n",
       "      <td>-4.704742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>55.981846</td>\n",
       "      <td>39.999140</td>\n",
       "      <td>18.927992</td>\n",
       "      <td>-6.851680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>-46.845830</td>\n",
       "      <td>-24.053850</td>\n",
       "      <td>-25.841563</td>\n",
       "      <td>6.837024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>-13.525147</td>\n",
       "      <td>-3.141838</td>\n",
       "      <td>-7.947771</td>\n",
       "      <td>-1.861003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>16.815744</td>\n",
       "      <td>7.308019</td>\n",
       "      <td>4.643840</td>\n",
       "      <td>3.868566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>6.106553</td>\n",
       "      <td>10.777969</td>\n",
       "      <td>-3.123699</td>\n",
       "      <td>-0.710507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>29.242251</td>\n",
       "      <td>16.785159</td>\n",
       "      <td>12.232568</td>\n",
       "      <td>-3.257455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>-5.383569</td>\n",
       "      <td>7.423333</td>\n",
       "      <td>-13.453364</td>\n",
       "      <td>1.257070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>13.307969</td>\n",
       "      <td>0.594557</td>\n",
       "      <td>13.116904</td>\n",
       "      <td>-1.850479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>24.593092</td>\n",
       "      <td>16.817142</td>\n",
       "      <td>4.891654</td>\n",
       "      <td>4.250240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>6.255285</td>\n",
       "      <td>-0.594501</td>\n",
       "      <td>-0.848112</td>\n",
       "      <td>7.097321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>45.332741</td>\n",
       "      <td>4.833390</td>\n",
       "      <td>-1.994883</td>\n",
       "      <td>39.287213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>-84.819030</td>\n",
       "      <td>-41.116845</td>\n",
       "      <td>-12.530102</td>\n",
       "      <td>-28.078693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>36.898649</td>\n",
       "      <td>41.789126</td>\n",
       "      <td>13.772212</td>\n",
       "      <td>-19.439474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>82.144338</td>\n",
       "      <td>80.974964</td>\n",
       "      <td>9.109184</td>\n",
       "      <td>-12.879440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>-109.300699</td>\n",
       "      <td>-79.406352</td>\n",
       "      <td>-22.556911</td>\n",
       "      <td>-4.156130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>75.106371</td>\n",
       "      <td>51.963637</td>\n",
       "      <td>8.436070</td>\n",
       "      <td>13.693844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>-66.504327</td>\n",
       "      <td>-87.249739</td>\n",
       "      <td>28.098475</td>\n",
       "      <td>-1.375140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>29.626587</td>\n",
       "      <td>2.422504</td>\n",
       "      <td>14.754220</td>\n",
       "      <td>9.348701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>-61.997831</td>\n",
       "      <td>-11.531267</td>\n",
       "      <td>-40.454905</td>\n",
       "      <td>-1.452298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>114.526615</td>\n",
       "      <td>85.614472</td>\n",
       "      <td>6.180990</td>\n",
       "      <td>1.979690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>-139.565547</td>\n",
       "      <td>-116.558870</td>\n",
       "      <td>-5.281693</td>\n",
       "      <td>-4.804461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>-207.460541</td>\n",
       "      <td>-89.646845</td>\n",
       "      <td>-89.230473</td>\n",
       "      <td>-3.515379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>446.119214</td>\n",
       "      <td>303.845191</td>\n",
       "      <td>88.955264</td>\n",
       "      <td>6.856045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total        Coal        Oil        Gas\n",
       "Year                                              \n",
       "1982  -12.841279  -10.150618  -1.842992   1.080880\n",
       "1983   16.405036   16.578460   0.069616  -0.593568\n",
       "1984  -17.458276  -20.422766   2.171750   0.043910\n",
       "1985   26.672126   22.822858   3.719961   0.102646\n",
       "1986   -7.315413   -1.913921  -5.557284   2.081218\n",
       "1987    0.316391   -1.197777   0.716183  -0.645022\n",
       "1988    7.315462    6.631124   0.130156  -0.036448\n",
       "1989   12.609745   -2.861897  11.154967   0.882811\n",
       "1990  -11.613852    4.434316  -9.646343  -0.732691\n",
       "1991    0.023925   -0.359129   0.803343  -0.113451\n",
       "1992    2.713090   -8.157152  13.376323   1.227315\n",
       "1993  -18.229812    3.334920 -22.808400  -3.953456\n",
       "1994   17.116525    0.891742  13.252688   2.260688\n",
       "1995    6.211880   -6.595733   5.249531   8.053295\n",
       "1996   18.570911   16.772907   9.552995  -9.357668\n",
       "1997  -30.017236   -9.551842 -25.837479   5.580427\n",
       "1998  -15.717379  -26.820691  17.648473  -4.704742\n",
       "1999   55.981846   39.999140  18.927992  -6.851680\n",
       "2000  -46.845830  -24.053850 -25.841563   6.837024\n",
       "2001  -13.525147   -3.141838  -7.947771  -1.861003\n",
       "2002   16.815744    7.308019   4.643840   3.868566\n",
       "2003    6.106553   10.777969  -3.123699  -0.710507\n",
       "2004   29.242251   16.785159  12.232568  -3.257455\n",
       "2005   -5.383569    7.423333 -13.453364   1.257070\n",
       "2006   13.307969    0.594557  13.116904  -1.850479\n",
       "2007   24.593092   16.817142   4.891654   4.250240\n",
       "2008    6.255285   -0.594501  -0.848112   7.097321\n",
       "2009   45.332741    4.833390  -1.994883  39.287213\n",
       "2010  -84.819030  -41.116845 -12.530102 -28.078693\n",
       "2011   36.898649   41.789126  13.772212 -19.439474\n",
       "2012   82.144338   80.974964   9.109184 -12.879440\n",
       "2013 -109.300699  -79.406352 -22.556911  -4.156130\n",
       "2014   75.106371   51.963637   8.436070  13.693844\n",
       "2015  -66.504327  -87.249739  28.098475  -1.375140\n",
       "2016   29.626587    2.422504  14.754220   9.348701\n",
       "2017  -61.997831  -11.531267 -40.454905  -1.452298\n",
       "2018  114.526615   85.614472   6.180990   1.979690\n",
       "2019 -139.565547 -116.558870  -5.281693  -4.804461\n",
       "2020 -207.460541  -89.646845 -89.230473  -3.515379\n",
       "2021  446.119214  303.845191  88.955264   6.856045"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_differenced_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "8a867253",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[446.119214, 303.845191,  88.955264,   6.856045]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the data to the inputed for the forecast\n",
    "forecast_input_all = df_differenced_all.values[-lag_order:]\n",
    "forecast_input_all # the output is the values of total, coal, oil and gas for 2019 to 2021 (this depends on the train test split and lag_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a86aaa8",
   "metadata": {},
   "source": [
    "### Fitting the model for the whole df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "25140568",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  Summary of Regression Results   \n",
       "==================================\n",
       "Model:                         VAR\n",
       "Method:                        OLS\n",
       "Date:           Thu, 09, Mar, 2023\n",
       "Time:                     14:48:00\n",
       "--------------------------------------------------------------------\n",
       "No. of Equations:         4.00000    BIC:                    21.4385\n",
       "Nobs:                     39.0000    HQIC:                   20.8915\n",
       "Log likelihood:          -602.770    FPE:                8.76187e+08\n",
       "AIC:                      20.5854    Det(Omega_mle):     5.40810e+08\n",
       "--------------------------------------------------------------------\n",
       "Results for equation Total\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const           -0.601355        10.754905           -0.056           0.955\n",
       "L1.Total        -6.984104         3.072087           -2.273           0.023\n",
       "L1.Coal          7.405509         3.381984            2.190           0.029\n",
       "L1.Oil           4.995131         3.472697            1.438           0.150\n",
       "L1.Gas           5.622706         3.406508            1.651           0.099\n",
       "===========================================================================\n",
       "\n",
       "Results for equation Coal\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            0.387088         6.904876            0.056           0.955\n",
       "L1.Total        -5.160229         1.972345           -2.616           0.009\n",
       "L1.Coal          5.321442         2.171306            2.451           0.014\n",
       "L1.Oil           4.008963         2.229545            1.798           0.072\n",
       "L1.Gas           3.985522         2.187050            1.822           0.068\n",
       "===========================================================================\n",
       "\n",
       "Results for equation Oil\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const           -0.875136         3.554456           -0.246           0.806\n",
       "L1.Total        -0.733718         1.015313           -0.723           0.470\n",
       "L1.Coal          0.907653         1.117733            0.812           0.417\n",
       "L1.Oil           0.106056         1.147713            0.092           0.926\n",
       "L1.Gas           0.668514         1.125838            0.594           0.553\n",
       "===========================================================================\n",
       "\n",
       "Results for equation Gas\n",
       "===========================================================================\n",
       "              coefficient       std. error           t-stat            prob\n",
       "---------------------------------------------------------------------------\n",
       "const            0.013991         1.659587            0.008           0.993\n",
       "L1.Total        -0.077867         0.474053           -0.164           0.870\n",
       "L1.Coal          0.055142         0.521873            0.106           0.916\n",
       "L1.Oil           0.084022         0.535871            0.157           0.875\n",
       "L1.Gas           0.017970         0.525657            0.034           0.973\n",
       "===========================================================================\n",
       "\n",
       "Correlation matrix of residuals\n",
       "            Total      Coal       Oil       Gas\n",
       "Total    1.000000  0.941259  0.801167  0.252019\n",
       "Coal     0.941259  1.000000  0.604421  0.083836\n",
       "Oil      0.801167  0.604421  1.000000  0.076407\n",
       "Gas      0.252019  0.083836  0.076407  1.000000\n",
       "\n"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_all = VAR(df_differenced_all)\n",
    "model_fitted_all = model_all.fit(p)\n",
    "model_fitted_all.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979841f4",
   "metadata": {},
   "source": [
    "### Forecasting the next 10 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "1d33ac55",
   "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>Total_2d</th>\n",
       "      <th>Coal_2d</th>\n",
       "      <th>Oil_2d</th>\n",
       "      <th>Gas_2d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>-383.323335</td>\n",
       "      <td>-300.852069</td>\n",
       "      <td>-38.397414</td>\n",
       "      <td>-10.372032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>198.486980</td>\n",
       "      <td>182.184378</td>\n",
       "      <td>-3.699144</td>\n",
       "      <td>9.860095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>-0.724314</td>\n",
       "      <td>-29.899527</td>\n",
       "      <td>25.050790</td>\n",
       "      <td>-5.529231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>-122.921149</td>\n",
       "      <td>-76.593083</td>\n",
       "      <td>-28.521662</td>\n",
       "      <td>0.427116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>150.614082</td>\n",
       "      <td>114.462630</td>\n",
       "      <td>17.055071</td>\n",
       "      <td>2.973253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>-102.941697</td>\n",
       "      <td>-87.486599</td>\n",
       "      <td>-3.694662</td>\n",
       "      <td>-3.915774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>29.998795</td>\n",
       "      <td>35.616704</td>\n",
       "      <td>-7.761978</td>\n",
       "      <td>2.824781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>30.754574</td>\n",
       "      <td>15.259430</td>\n",
       "      <td>10.507000</td>\n",
       "      <td>-0.959365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>-55.301041</td>\n",
       "      <td>-38.812765</td>\n",
       "      <td>-9.117083</td>\n",
       "      <td>-0.673770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>48.869150</td>\n",
       "      <td>39.977831</td>\n",
       "      <td>3.054387</td>\n",
       "      <td>1.401771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Total_2d     Coal_2d     Oil_2d     Gas_2d\n",
       "2022 -383.323335 -300.852069 -38.397414 -10.372032\n",
       "2023  198.486980  182.184378  -3.699144   9.860095\n",
       "2024   -0.724314  -29.899527  25.050790  -5.529231\n",
       "2025 -122.921149  -76.593083 -28.521662   0.427116\n",
       "2026  150.614082  114.462630  17.055071   2.973253\n",
       "2027 -102.941697  -87.486599  -3.694662  -3.915774\n",
       "2028   29.998795   35.616704  -7.761978   2.824781\n",
       "2029   30.754574   15.259430  10.507000  -0.959365\n",
       "2030  -55.301041  -38.812765  -9.117083  -0.673770\n",
       "2031   48.869150   39.977831   3.054387   1.401771"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = df_differenced_all.last_valid_index() # gives the index of the last row in df_differenced_all\n",
    "s = 10 # steps to future\n",
    "fc_all = model_fitted_all.forecast(y=forecast_input_all, steps=s) \n",
    "df_forecast_all = pd.DataFrame(fc_all, index=list(range(l+1,l+s+1)), columns=df.columns + '_2d')\n",
    "df_forecast_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "ffc36e22",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Tranforming the future forecast back\n",
    "df_results_all = invert_transformation(df, df_forecast_all, second_diff=True) \n",
    "df_results_all = df_results_all.loc[:, ['Total_forecast', 'Coal_forecast', 'Oil_forecast','Gas_forecast']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b96accf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Total_forecast</th>\n",
       "      <th>Coal_forecast</th>\n",
       "      <th>Oil_forecast</th>\n",
       "      <th>Gas_forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2591.031973</td>\n",
       "      <td>1715.711518</td>\n",
       "      <td>602.296668</td>\n",
       "      <td>128.894210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2670.867302</td>\n",
       "      <td>1811.295516</td>\n",
       "      <td>578.746071</td>\n",
       "      <td>134.197704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>2749.978317</td>\n",
       "      <td>1876.979988</td>\n",
       "      <td>580.246263</td>\n",
       "      <td>133.971967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>2706.168183</td>\n",
       "      <td>1866.071377</td>\n",
       "      <td>553.224794</td>\n",
       "      <td>134.173347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>2812.972131</td>\n",
       "      <td>1969.625396</td>\n",
       "      <td>543.258395</td>\n",
       "      <td>137.347979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2816.834382</td>\n",
       "      <td>1985.692816</td>\n",
       "      <td>529.597334</td>\n",
       "      <td>136.606837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>2850.695429</td>\n",
       "      <td>2037.376939</td>\n",
       "      <td>508.174295</td>\n",
       "      <td>138.690476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>2915.311050</td>\n",
       "      <td>2104.320493</td>\n",
       "      <td>497.258256</td>\n",
       "      <td>139.814750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2924.625630</td>\n",
       "      <td>2132.451281</td>\n",
       "      <td>477.225133</td>\n",
       "      <td>140.265253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2982.809359</td>\n",
       "      <td>2200.559900</td>\n",
       "      <td>460.246398</td>\n",
       "      <td>142.117528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Total_forecast  Coal_forecast  Oil_forecast  Gas_forecast\n",
       "2022     2591.031973    1715.711518    602.296668    128.894210\n",
       "2023     2670.867302    1811.295516    578.746071    134.197704\n",
       "2024     2749.978317    1876.979988    580.246263    133.971967\n",
       "2025     2706.168183    1866.071377    553.224794    134.173347\n",
       "2026     2812.972131    1969.625396    543.258395    137.347979\n",
       "2027     2816.834382    1985.692816    529.597334    136.606837\n",
       "2028     2850.695429    2037.376939    508.174295    138.690476\n",
       "2029     2915.311050    2104.320493    497.258256    139.814750\n",
       "2030     2924.625630    2132.451281    477.225133    140.265253\n",
       "2031     2982.809359    2200.559900    460.246398    142.117528"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "1d41b6ad",
   "metadata": {
    "scrolled": true
   },
   "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>Total</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>291.711364</td>\n",
       "      <td>197.337053</td>\n",
       "      <td>83.176464</td>\n",
       "      <td>2.487856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>314.966469</td>\n",
       "      <td>210.305446</td>\n",
       "      <td>89.713040</td>\n",
       "      <td>2.883568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>325.380295</td>\n",
       "      <td>213.123221</td>\n",
       "      <td>94.406624</td>\n",
       "      <td>4.360160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>352.199157</td>\n",
       "      <td>232.519456</td>\n",
       "      <td>99.169824</td>\n",
       "      <td>5.243184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>361.559743</td>\n",
       "      <td>231.492925</td>\n",
       "      <td>106.104774</td>\n",
       "      <td>6.170118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>397.592455</td>\n",
       "      <td>253.289252</td>\n",
       "      <td>116.759685</td>\n",
       "      <td>7.199698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>426.309754</td>\n",
       "      <td>273.171658</td>\n",
       "      <td>121.857312</td>\n",
       "      <td>10.310496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>455.343444</td>\n",
       "      <td>291.856287</td>\n",
       "      <td>127.671122</td>\n",
       "      <td>12.776272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>491.692596</td>\n",
       "      <td>317.172040</td>\n",
       "      <td>133.615088</td>\n",
       "      <td>15.205600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>540.651493</td>\n",
       "      <td>339.625896</td>\n",
       "      <td>150.714021</td>\n",
       "      <td>18.517739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>577.996538</td>\n",
       "      <td>366.514068</td>\n",
       "      <td>158.166611</td>\n",
       "      <td>21.097187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>615.365508</td>\n",
       "      <td>393.043111</td>\n",
       "      <td>166.422544</td>\n",
       "      <td>23.563184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>655.447568</td>\n",
       "      <td>411.415002</td>\n",
       "      <td>188.054800</td>\n",
       "      <td>27.256496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>677.299816</td>\n",
       "      <td>433.121813</td>\n",
       "      <td>186.878656</td>\n",
       "      <td>26.996352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>716.268589</td>\n",
       "      <td>455.720366</td>\n",
       "      <td>198.955200</td>\n",
       "      <td>28.996896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>761.449242</td>\n",
       "      <td>471.723186</td>\n",
       "      <td>216.281275</td>\n",
       "      <td>39.050735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>825.200806</td>\n",
       "      <td>504.498913</td>\n",
       "      <td>243.160345</td>\n",
       "      <td>39.746906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>858.935134</td>\n",
       "      <td>527.722798</td>\n",
       "      <td>244.201936</td>\n",
       "      <td>46.023504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>876.952083</td>\n",
       "      <td>524.125992</td>\n",
       "      <td>262.892000</td>\n",
       "      <td>47.595360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>950.950878</td>\n",
       "      <td>560.528326</td>\n",
       "      <td>300.510056</td>\n",
       "      <td>42.315536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>978.103843</td>\n",
       "      <td>572.876810</td>\n",
       "      <td>312.286549</td>\n",
       "      <td>43.872736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>991.731661</td>\n",
       "      <td>582.083456</td>\n",
       "      <td>316.115271</td>\n",
       "      <td>43.568933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1022.175223</td>\n",
       "      <td>598.598121</td>\n",
       "      <td>324.587833</td>\n",
       "      <td>47.133696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1058.725338</td>\n",
       "      <td>625.890755</td>\n",
       "      <td>329.936696</td>\n",
       "      <td>49.987952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1124.517704</td>\n",
       "      <td>669.968548</td>\n",
       "      <td>347.518127</td>\n",
       "      <td>49.584753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1184.926501</td>\n",
       "      <td>721.469674</td>\n",
       "      <td>351.646194</td>\n",
       "      <td>50.438624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1258.643267</td>\n",
       "      <td>773.565357</td>\n",
       "      <td>368.891165</td>\n",
       "      <td>49.442016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1356.953125</td>\n",
       "      <td>842.478182</td>\n",
       "      <td>391.027790</td>\n",
       "      <td>52.695648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1461.518268</td>\n",
       "      <td>910.796506</td>\n",
       "      <td>412.316303</td>\n",
       "      <td>63.046601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1611.416152</td>\n",
       "      <td>983.948220</td>\n",
       "      <td>431.609933</td>\n",
       "      <td>112.684767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1676.495006</td>\n",
       "      <td>1015.983089</td>\n",
       "      <td>438.373461</td>\n",
       "      <td>134.244240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1778.472509</td>\n",
       "      <td>1089.807084</td>\n",
       "      <td>458.909201</td>\n",
       "      <td>136.364239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1962.594350</td>\n",
       "      <td>1244.606043</td>\n",
       "      <td>488.554125</td>\n",
       "      <td>125.604798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2037.415492</td>\n",
       "      <td>1319.998650</td>\n",
       "      <td>495.642138</td>\n",
       "      <td>110.689227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2187.343005</td>\n",
       "      <td>1447.354894</td>\n",
       "      <td>511.166221</td>\n",
       "      <td>109.467500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2270.766191</td>\n",
       "      <td>1487.461399</td>\n",
       "      <td>554.788779</td>\n",
       "      <td>106.870633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2383.815964</td>\n",
       "      <td>1529.990408</td>\n",
       "      <td>613.165557</td>\n",
       "      <td>113.622467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2434.867906</td>\n",
       "      <td>1560.988150</td>\n",
       "      <td>631.087430</td>\n",
       "      <td>118.922003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2600.446463</td>\n",
       "      <td>1677.600364</td>\n",
       "      <td>655.190293</td>\n",
       "      <td>126.201229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2626.459473</td>\n",
       "      <td>1677.653708</td>\n",
       "      <td>674.011463</td>\n",
       "      <td>128.675994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2445.011942</td>\n",
       "      <td>1588.060207</td>\n",
       "      <td>603.602160</td>\n",
       "      <td>127.635380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2709.683625</td>\n",
       "      <td>1802.311897</td>\n",
       "      <td>622.148121</td>\n",
       "      <td>133.450811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2591.031973</td>\n",
       "      <td>1715.711518</td>\n",
       "      <td>602.296668</td>\n",
       "      <td>128.894210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2670.867302</td>\n",
       "      <td>1811.295516</td>\n",
       "      <td>578.746071</td>\n",
       "      <td>134.197704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>2749.978317</td>\n",
       "      <td>1876.979988</td>\n",
       "      <td>580.246263</td>\n",
       "      <td>133.971967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>2706.168183</td>\n",
       "      <td>1866.071377</td>\n",
       "      <td>553.224794</td>\n",
       "      <td>134.173347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>2812.972131</td>\n",
       "      <td>1969.625396</td>\n",
       "      <td>543.258395</td>\n",
       "      <td>137.347979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2816.834382</td>\n",
       "      <td>1985.692816</td>\n",
       "      <td>529.597334</td>\n",
       "      <td>136.606837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>2850.695429</td>\n",
       "      <td>2037.376939</td>\n",
       "      <td>508.174295</td>\n",
       "      <td>138.690476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>2915.311050</td>\n",
       "      <td>2104.320493</td>\n",
       "      <td>497.258256</td>\n",
       "      <td>139.814750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2924.625630</td>\n",
       "      <td>2132.451281</td>\n",
       "      <td>477.225133</td>\n",
       "      <td>140.265253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2982.809359</td>\n",
       "      <td>2200.559900</td>\n",
       "      <td>460.246398</td>\n",
       "      <td>142.117528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total         Coal         Oil         Gas\n",
       "1980   291.711364   197.337053   83.176464    2.487856\n",
       "1981   314.966469   210.305446   89.713040    2.883568\n",
       "1982   325.380295   213.123221   94.406624    4.360160\n",
       "1983   352.199157   232.519456   99.169824    5.243184\n",
       "1984   361.559743   231.492925  106.104774    6.170118\n",
       "1985   397.592455   253.289252  116.759685    7.199698\n",
       "1986   426.309754   273.171658  121.857312   10.310496\n",
       "1987   455.343444   291.856287  127.671122   12.776272\n",
       "1988   491.692596   317.172040  133.615088   15.205600\n",
       "1989   540.651493   339.625896  150.714021   18.517739\n",
       "1990   577.996538   366.514068  158.166611   21.097187\n",
       "1991   615.365508   393.043111  166.422544   23.563184\n",
       "1992   655.447568   411.415002  188.054800   27.256496\n",
       "1993   677.299816   433.121813  186.878656   26.996352\n",
       "1994   716.268589   455.720366  198.955200   28.996896\n",
       "1995   761.449242   471.723186  216.281275   39.050735\n",
       "1996   825.200806   504.498913  243.160345   39.746906\n",
       "1997   858.935134   527.722798  244.201936   46.023504\n",
       "1998   876.952083   524.125992  262.892000   47.595360\n",
       "1999   950.950878   560.528326  300.510056   42.315536\n",
       "2000   978.103843   572.876810  312.286549   43.872736\n",
       "2001   991.731661   582.083456  316.115271   43.568933\n",
       "2002  1022.175223   598.598121  324.587833   47.133696\n",
       "2003  1058.725338   625.890755  329.936696   49.987952\n",
       "2004  1124.517704   669.968548  347.518127   49.584753\n",
       "2005  1184.926501   721.469674  351.646194   50.438624\n",
       "2006  1258.643267   773.565357  368.891165   49.442016\n",
       "2007  1356.953125   842.478182  391.027790   52.695648\n",
       "2008  1461.518268   910.796506  412.316303   63.046601\n",
       "2009  1611.416152   983.948220  431.609933  112.684767\n",
       "2010  1676.495006  1015.983089  438.373461  134.244240\n",
       "2011  1778.472509  1089.807084  458.909201  136.364239\n",
       "2012  1962.594350  1244.606043  488.554125  125.604798\n",
       "2013  2037.415492  1319.998650  495.642138  110.689227\n",
       "2014  2187.343005  1447.354894  511.166221  109.467500\n",
       "2015  2270.766191  1487.461399  554.788779  106.870633\n",
       "2016  2383.815964  1529.990408  613.165557  113.622467\n",
       "2017  2434.867906  1560.988150  631.087430  118.922003\n",
       "2018  2600.446463  1677.600364  655.190293  126.201229\n",
       "2019  2626.459473  1677.653708  674.011463  128.675994\n",
       "2020  2445.011942  1588.060207  603.602160  127.635380\n",
       "2021  2709.683625  1802.311897  622.148121  133.450811\n",
       "2022  2591.031973  1715.711518  602.296668  128.894210\n",
       "2023  2670.867302  1811.295516  578.746071  134.197704\n",
       "2024  2749.978317  1876.979988  580.246263  133.971967\n",
       "2025  2706.168183  1866.071377  553.224794  134.173347\n",
       "2026  2812.972131  1969.625396  543.258395  137.347979\n",
       "2027  2816.834382  1985.692816  529.597334  136.606837\n",
       "2028  2850.695429  2037.376939  508.174295  138.690476\n",
       "2029  2915.311050  2104.320493  497.258256  139.814750\n",
       "2030  2924.625630  2132.451281  477.225133  140.265253\n",
       "2031  2982.809359  2200.559900  460.246398  142.117528"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concate the actual df with the forecasted df till 2031 (df_results_all)\n",
    "df_results_all = df_results_all.rename(columns={\"Total_forecast\": \"Total\", \"Coal_forecast\": \"Coal\", \"Oil_forecast\": \"Oil\", \"Gas_forecast\": \"Gas\"})\n",
    "# renaming the df_results_all columns to that of df\n",
    "\n",
    "df_future  = pd.concat([df, df_results_all], axis=0)\n",
    "df_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "704ac2d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fb04b31b520>"
      ]
     },
     "execution_count": 115,
     "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": [
    "# Plot\n",
    "plt.figure()\n",
    "\n",
    "x = df_future.index\n",
    "y1 = df_future['Total']\n",
    "y2 = df_future['Coal']\n",
    "y3 = df_future['Oil']\n",
    "y4 = df_future['Gas']\n",
    "\n",
    "plt.plot(x,y1)\n",
    "plt.plot(x,y2)\n",
    "plt.plot(x,y3)\n",
    "plt.plot(x,y4)\n",
    "\n",
    "plt.legend(['Total', 'Coal', 'Oil', 'Gas'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "19da9595",
   "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>Total</th>\n",
       "      <th>Coal</th>\n",
       "      <th>Oil</th>\n",
       "      <th>Gas</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>291.711364</td>\n",
       "      <td>197.337053</td>\n",
       "      <td>83.176464</td>\n",
       "      <td>2.487856</td>\n",
       "      <td>1980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>314.966469</td>\n",
       "      <td>210.305446</td>\n",
       "      <td>89.713040</td>\n",
       "      <td>2.883568</td>\n",
       "      <td>1981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>325.380295</td>\n",
       "      <td>213.123221</td>\n",
       "      <td>94.406624</td>\n",
       "      <td>4.360160</td>\n",
       "      <td>1982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>352.199157</td>\n",
       "      <td>232.519456</td>\n",
       "      <td>99.169824</td>\n",
       "      <td>5.243184</td>\n",
       "      <td>1983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>361.559743</td>\n",
       "      <td>231.492925</td>\n",
       "      <td>106.104774</td>\n",
       "      <td>6.170118</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>397.592455</td>\n",
       "      <td>253.289252</td>\n",
       "      <td>116.759685</td>\n",
       "      <td>7.199698</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>426.309754</td>\n",
       "      <td>273.171658</td>\n",
       "      <td>121.857312</td>\n",
       "      <td>10.310496</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>455.343444</td>\n",
       "      <td>291.856287</td>\n",
       "      <td>127.671122</td>\n",
       "      <td>12.776272</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>491.692596</td>\n",
       "      <td>317.172040</td>\n",
       "      <td>133.615088</td>\n",
       "      <td>15.205600</td>\n",
       "      <td>1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>540.651493</td>\n",
       "      <td>339.625896</td>\n",
       "      <td>150.714021</td>\n",
       "      <td>18.517739</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>577.996538</td>\n",
       "      <td>366.514068</td>\n",
       "      <td>158.166611</td>\n",
       "      <td>21.097187</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>615.365508</td>\n",
       "      <td>393.043111</td>\n",
       "      <td>166.422544</td>\n",
       "      <td>23.563184</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>655.447568</td>\n",
       "      <td>411.415002</td>\n",
       "      <td>188.054800</td>\n",
       "      <td>27.256496</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>677.299816</td>\n",
       "      <td>433.121813</td>\n",
       "      <td>186.878656</td>\n",
       "      <td>26.996352</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>716.268589</td>\n",
       "      <td>455.720366</td>\n",
       "      <td>198.955200</td>\n",
       "      <td>28.996896</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>761.449242</td>\n",
       "      <td>471.723186</td>\n",
       "      <td>216.281275</td>\n",
       "      <td>39.050735</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>825.200806</td>\n",
       "      <td>504.498913</td>\n",
       "      <td>243.160345</td>\n",
       "      <td>39.746906</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>858.935134</td>\n",
       "      <td>527.722798</td>\n",
       "      <td>244.201936</td>\n",
       "      <td>46.023504</td>\n",
       "      <td>1997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>876.952083</td>\n",
       "      <td>524.125992</td>\n",
       "      <td>262.892000</td>\n",
       "      <td>47.595360</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>950.950878</td>\n",
       "      <td>560.528326</td>\n",
       "      <td>300.510056</td>\n",
       "      <td>42.315536</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>978.103843</td>\n",
       "      <td>572.876810</td>\n",
       "      <td>312.286549</td>\n",
       "      <td>43.872736</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>991.731661</td>\n",
       "      <td>582.083456</td>\n",
       "      <td>316.115271</td>\n",
       "      <td>43.568933</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1022.175223</td>\n",
       "      <td>598.598121</td>\n",
       "      <td>324.587833</td>\n",
       "      <td>47.133696</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1058.725338</td>\n",
       "      <td>625.890755</td>\n",
       "      <td>329.936696</td>\n",
       "      <td>49.987952</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1124.517704</td>\n",
       "      <td>669.968548</td>\n",
       "      <td>347.518127</td>\n",
       "      <td>49.584753</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1184.926501</td>\n",
       "      <td>721.469674</td>\n",
       "      <td>351.646194</td>\n",
       "      <td>50.438624</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1258.643267</td>\n",
       "      <td>773.565357</td>\n",
       "      <td>368.891165</td>\n",
       "      <td>49.442016</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1356.953125</td>\n",
       "      <td>842.478182</td>\n",
       "      <td>391.027790</td>\n",
       "      <td>52.695648</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1461.518268</td>\n",
       "      <td>910.796506</td>\n",
       "      <td>412.316303</td>\n",
       "      <td>63.046601</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1611.416152</td>\n",
       "      <td>983.948220</td>\n",
       "      <td>431.609933</td>\n",
       "      <td>112.684767</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>1676.495006</td>\n",
       "      <td>1015.983089</td>\n",
       "      <td>438.373461</td>\n",
       "      <td>134.244240</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1778.472509</td>\n",
       "      <td>1089.807084</td>\n",
       "      <td>458.909201</td>\n",
       "      <td>136.364239</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1962.594350</td>\n",
       "      <td>1244.606043</td>\n",
       "      <td>488.554125</td>\n",
       "      <td>125.604798</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2037.415492</td>\n",
       "      <td>1319.998650</td>\n",
       "      <td>495.642138</td>\n",
       "      <td>110.689227</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2187.343005</td>\n",
       "      <td>1447.354894</td>\n",
       "      <td>511.166221</td>\n",
       "      <td>109.467500</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>2270.766191</td>\n",
       "      <td>1487.461399</td>\n",
       "      <td>554.788779</td>\n",
       "      <td>106.870633</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>2383.815964</td>\n",
       "      <td>1529.990408</td>\n",
       "      <td>613.165557</td>\n",
       "      <td>113.622467</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>2434.867906</td>\n",
       "      <td>1560.988150</td>\n",
       "      <td>631.087430</td>\n",
       "      <td>118.922003</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>2600.446463</td>\n",
       "      <td>1677.600364</td>\n",
       "      <td>655.190293</td>\n",
       "      <td>126.201229</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>2626.459473</td>\n",
       "      <td>1677.653708</td>\n",
       "      <td>674.011463</td>\n",
       "      <td>128.675994</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>2445.011942</td>\n",
       "      <td>1588.060207</td>\n",
       "      <td>603.602160</td>\n",
       "      <td>127.635380</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>2709.683625</td>\n",
       "      <td>1802.311897</td>\n",
       "      <td>622.148121</td>\n",
       "      <td>133.450811</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>2591.031973</td>\n",
       "      <td>1715.711518</td>\n",
       "      <td>602.296668</td>\n",
       "      <td>128.894210</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>2670.867302</td>\n",
       "      <td>1811.295516</td>\n",
       "      <td>578.746071</td>\n",
       "      <td>134.197704</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>2749.978317</td>\n",
       "      <td>1876.979988</td>\n",
       "      <td>580.246263</td>\n",
       "      <td>133.971967</td>\n",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>2706.168183</td>\n",
       "      <td>1866.071377</td>\n",
       "      <td>553.224794</td>\n",
       "      <td>134.173347</td>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026</th>\n",
       "      <td>2812.972131</td>\n",
       "      <td>1969.625396</td>\n",
       "      <td>543.258395</td>\n",
       "      <td>137.347979</td>\n",
       "      <td>2026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2027</th>\n",
       "      <td>2816.834382</td>\n",
       "      <td>1985.692816</td>\n",
       "      <td>529.597334</td>\n",
       "      <td>136.606837</td>\n",
       "      <td>2027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2028</th>\n",
       "      <td>2850.695429</td>\n",
       "      <td>2037.376939</td>\n",
       "      <td>508.174295</td>\n",
       "      <td>138.690476</td>\n",
       "      <td>2028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2029</th>\n",
       "      <td>2915.311050</td>\n",
       "      <td>2104.320493</td>\n",
       "      <td>497.258256</td>\n",
       "      <td>139.814750</td>\n",
       "      <td>2029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2030</th>\n",
       "      <td>2924.625630</td>\n",
       "      <td>2132.451281</td>\n",
       "      <td>477.225133</td>\n",
       "      <td>140.265253</td>\n",
       "      <td>2030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2031</th>\n",
       "      <td>2982.809359</td>\n",
       "      <td>2200.559900</td>\n",
       "      <td>460.246398</td>\n",
       "      <td>142.117528</td>\n",
       "      <td>2031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total         Coal         Oil         Gas  Year\n",
       "1980   291.711364   197.337053   83.176464    2.487856  1980\n",
       "1981   314.966469   210.305446   89.713040    2.883568  1981\n",
       "1982   325.380295   213.123221   94.406624    4.360160  1982\n",
       "1983   352.199157   232.519456   99.169824    5.243184  1983\n",
       "1984   361.559743   231.492925  106.104774    6.170118  1984\n",
       "1985   397.592455   253.289252  116.759685    7.199698  1985\n",
       "1986   426.309754   273.171658  121.857312   10.310496  1986\n",
       "1987   455.343444   291.856287  127.671122   12.776272  1987\n",
       "1988   491.692596   317.172040  133.615088   15.205600  1988\n",
       "1989   540.651493   339.625896  150.714021   18.517739  1989\n",
       "1990   577.996538   366.514068  158.166611   21.097187  1990\n",
       "1991   615.365508   393.043111  166.422544   23.563184  1991\n",
       "1992   655.447568   411.415002  188.054800   27.256496  1992\n",
       "1993   677.299816   433.121813  186.878656   26.996352  1993\n",
       "1994   716.268589   455.720366  198.955200   28.996896  1994\n",
       "1995   761.449242   471.723186  216.281275   39.050735  1995\n",
       "1996   825.200806   504.498913  243.160345   39.746906  1996\n",
       "1997   858.935134   527.722798  244.201936   46.023504  1997\n",
       "1998   876.952083   524.125992  262.892000   47.595360  1998\n",
       "1999   950.950878   560.528326  300.510056   42.315536  1999\n",
       "2000   978.103843   572.876810  312.286549   43.872736  2000\n",
       "2001   991.731661   582.083456  316.115271   43.568933  2001\n",
       "2002  1022.175223   598.598121  324.587833   47.133696  2002\n",
       "2003  1058.725338   625.890755  329.936696   49.987952  2003\n",
       "2004  1124.517704   669.968548  347.518127   49.584753  2004\n",
       "2005  1184.926501   721.469674  351.646194   50.438624  2005\n",
       "2006  1258.643267   773.565357  368.891165   49.442016  2006\n",
       "2007  1356.953125   842.478182  391.027790   52.695648  2007\n",
       "2008  1461.518268   910.796506  412.316303   63.046601  2008\n",
       "2009  1611.416152   983.948220  431.609933  112.684767  2009\n",
       "2010  1676.495006  1015.983089  438.373461  134.244240  2010\n",
       "2011  1778.472509  1089.807084  458.909201  136.364239  2011\n",
       "2012  1962.594350  1244.606043  488.554125  125.604798  2012\n",
       "2013  2037.415492  1319.998650  495.642138  110.689227  2013\n",
       "2014  2187.343005  1447.354894  511.166221  109.467500  2014\n",
       "2015  2270.766191  1487.461399  554.788779  106.870633  2015\n",
       "2016  2383.815964  1529.990408  613.165557  113.622467  2016\n",
       "2017  2434.867906  1560.988150  631.087430  118.922003  2017\n",
       "2018  2600.446463  1677.600364  655.190293  126.201229  2018\n",
       "2019  2626.459473  1677.653708  674.011463  128.675994  2019\n",
       "2020  2445.011942  1588.060207  603.602160  127.635380  2020\n",
       "2021  2709.683625  1802.311897  622.148121  133.450811  2021\n",
       "2022  2591.031973  1715.711518  602.296668  128.894210  2022\n",
       "2023  2670.867302  1811.295516  578.746071  134.197704  2023\n",
       "2024  2749.978317  1876.979988  580.246263  133.971967  2024\n",
       "2025  2706.168183  1866.071377  553.224794  134.173347  2025\n",
       "2026  2812.972131  1969.625396  543.258395  137.347979  2026\n",
       "2027  2816.834382  1985.692816  529.597334  136.606837  2027\n",
       "2028  2850.695429  2037.376939  508.174295  138.690476  2028\n",
       "2029  2915.311050  2104.320493  497.258256  139.814750  2029\n",
       "2030  2924.625630  2132.451281  477.225133  140.265253  2030\n",
       "2031  2982.809359  2200.559900  460.246398  142.117528  2031"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_future[\"Year\"]=df_future.index\n",
    "df_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "6db2c025",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save to csv\n",
    "df_future.to_csv('Data/India_future.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "e0c5fa69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.079617116837376"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = df_future.last_valid_index() # gives the index of the last row \n",
    "total_2031 = df_future.loc[lr].at[\"Total\"]\n",
    "total_2021 = df_future.loc[lr-10].at[\"Total\"] # lr-10 gives the index of the row at 2021, i.e., 2021\n",
    "percent_increase = 100*(total_2031-total_2021)/total_2021 # percentage increase\n",
    "percent_increase"
   ]
  }
 ],
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