STAGE_E.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d0d3813",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Importing plotly failed. Interactive plots will not work.\n"
     ]
    }
   ],
   "source": [
    "#importing required libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from prophet import Prophet\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from math import sqrt\n",
    "from sklearn import metrics\n",
    "from prophet.plot import plot_yearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8303e430",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('https://github.com/HamoyeHQ/HDSC-Time-series-analysis-and-forecast/files/7768846/Time_series_analysis_and_forecast_DATASET.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "945a5005",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FullDate</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>SysLoad</th>\n",
       "      <th>GasPrice</th>\n",
       "      <th>ElecPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-09-01 00:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1688.21500</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>23.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-09-01 01:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1669.19500</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>22.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-09-01 02:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1491.98000</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>22.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-09-01 03:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1330.76000</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>18.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-09-01 04:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1247.94000</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>18.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92011</th>\n",
       "      <td>2021-02-28 19:00:00</td>\n",
       "      <td>24.3</td>\n",
       "      <td>449.63625</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>117.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92012</th>\n",
       "      <td>2021-02-28 20:00:00</td>\n",
       "      <td>24.3</td>\n",
       "      <td>815.59975</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>87.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92013</th>\n",
       "      <td>2021-02-28 21:00:00</td>\n",
       "      <td>24.3</td>\n",
       "      <td>745.12325</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>62.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92014</th>\n",
       "      <td>2021-02-28 22:00:00</td>\n",
       "      <td>24.3</td>\n",
       "      <td>694.95150</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92015</th>\n",
       "      <td>2021-02-28 23:00:00</td>\n",
       "      <td>24.3</td>\n",
       "      <td>714.69630</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>62.955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92016 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  FullDate  Tmax     SysLoad  GasPrice  ElecPrice\n",
       "0      2010-09-01 00:00:00  15.8  1688.21500    3.6900     23.710\n",
       "1      2010-09-01 01:00:00  15.8  1669.19500    3.6900     22.635\n",
       "2      2010-09-01 02:00:00  15.8  1491.98000    3.6900     22.565\n",
       "3      2010-09-01 03:00:00  15.8  1330.76000    3.6900     18.910\n",
       "4      2010-09-01 04:00:00  15.8  1247.94000    3.6900     18.030\n",
       "...                    ...   ...         ...       ...        ...\n",
       "92011  2021-02-28 19:00:00  24.3   449.63625    5.4848    117.925\n",
       "92012  2021-02-28 20:00:00  24.3   815.59975    5.4848     87.880\n",
       "92013  2021-02-28 21:00:00  24.3   745.12325    5.4848     62.060\n",
       "92014  2021-02-28 22:00:00  24.3   694.95150    5.4848     62.000\n",
       "92015  2021-02-28 23:00:00  24.3   714.69630    5.4848     62.955\n",
       "\n",
       "[92016 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f2d4b6fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tmax</th>\n",
       "      <th>SysLoad</th>\n",
       "      <th>GasPrice</th>\n",
       "      <th>ElecPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>92016.000000</td>\n",
       "      <td>92016.000000</td>\n",
       "      <td>92016.000000</td>\n",
       "      <td>92016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>231.696688</td>\n",
       "      <td>797.623357</td>\n",
       "      <td>6.016163</td>\n",
       "      <td>65.798356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4562.128748</td>\n",
       "      <td>613.520156</td>\n",
       "      <td>2.608116</td>\n",
       "      <td>213.144084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.900000</td>\n",
       "      <td>-1596.810100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-924.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.300000</td>\n",
       "      <td>417.119800</td>\n",
       "      <td>3.936900</td>\n",
       "      <td>31.543750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>21.900000</td>\n",
       "      <td>856.467925</td>\n",
       "      <td>5.165600</td>\n",
       "      <td>48.715000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.600000</td>\n",
       "      <td>1221.293675</td>\n",
       "      <td>8.023300</td>\n",
       "      <td>76.495000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99999.900000</td>\n",
       "      <td>3264.166300</td>\n",
       "      <td>29.990000</td>\n",
       "      <td>14700.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Tmax       SysLoad      GasPrice     ElecPrice\n",
       "count  92016.000000  92016.000000  92016.000000  92016.000000\n",
       "mean     231.696688    797.623357      6.016163     65.798356\n",
       "std     4562.128748    613.520156      2.608116    213.144084\n",
       "min        9.900000  -1596.810100      0.000000   -924.820000\n",
       "25%       17.300000    417.119800      3.936900     31.543750\n",
       "50%       21.900000    856.467925      5.165600     48.715000\n",
       "75%       27.600000   1221.293675      8.023300     76.495000\n",
       "max    99999.900000   3264.166300     29.990000  14700.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acc539ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FullDate     0\n",
       "Tmax         0\n",
       "SysLoad      0\n",
       "GasPrice     0\n",
       "ElecPrice    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null value\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0175d623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tmax</th>\n",
       "      <th>SysLoad</th>\n",
       "      <th>GasPrice</th>\n",
       "      <th>ElecPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tmax</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.009796</td>\n",
       "      <td>0.030173</td>\n",
       "      <td>0.002126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SysLoad</th>\n",
       "      <td>-0.009796</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.214574</td>\n",
       "      <td>0.138690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GasPrice</th>\n",
       "      <td>0.030173</td>\n",
       "      <td>-0.214574</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.157768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ElecPrice</th>\n",
       "      <td>0.002126</td>\n",
       "      <td>0.138690</td>\n",
       "      <td>0.157768</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "               Tmax   SysLoad  GasPrice  ElecPrice\n",
       "Tmax       1.000000 -0.009796  0.030173   0.002126\n",
       "SysLoad   -0.009796  1.000000 -0.214574   0.138690\n",
       "GasPrice   0.030173 -0.214574  1.000000   0.157768\n",
       "ElecPrice  0.002126  0.138690  0.157768   1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for correlation\n",
    "dataset.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95cd534e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting our date column\n",
    "dataset['FullDate'] = pd.to_datetime(dataset['FullDate'])\n",
    "dataset = dataset.set_index('FullDate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7790a83",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tmax</th>\n",
       "      <th>SysLoad</th>\n",
       "      <th>GasPrice</th>\n",
       "      <th>ElecPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullDate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-09-01 00:00:00</th>\n",
       "      <td>15.8</td>\n",
       "      <td>1688.21500</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>23.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01 01:00:00</th>\n",
       "      <td>15.8</td>\n",
       "      <td>1669.19500</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>22.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01 02:00:00</th>\n",
       "      <td>15.8</td>\n",
       "      <td>1491.98000</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>22.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01 03:00:00</th>\n",
       "      <td>15.8</td>\n",
       "      <td>1330.76000</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>18.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01 04:00:00</th>\n",
       "      <td>15.8</td>\n",
       "      <td>1247.94000</td>\n",
       "      <td>3.6900</td>\n",
       "      <td>18.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28 19:00:00</th>\n",
       "      <td>24.3</td>\n",
       "      <td>449.63625</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>117.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28 20:00:00</th>\n",
       "      <td>24.3</td>\n",
       "      <td>815.59975</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>87.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28 21:00:00</th>\n",
       "      <td>24.3</td>\n",
       "      <td>745.12325</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>62.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28 22:00:00</th>\n",
       "      <td>24.3</td>\n",
       "      <td>694.95150</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-28 23:00:00</th>\n",
       "      <td>24.3</td>\n",
       "      <td>714.69630</td>\n",
       "      <td>5.4848</td>\n",
       "      <td>62.955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92016 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Tmax     SysLoad  GasPrice  ElecPrice\n",
       "FullDate                                                  \n",
       "2010-09-01 00:00:00  15.8  1688.21500    3.6900     23.710\n",
       "2010-09-01 01:00:00  15.8  1669.19500    3.6900     22.635\n",
       "2010-09-01 02:00:00  15.8  1491.98000    3.6900     22.565\n",
       "2010-09-01 03:00:00  15.8  1330.76000    3.6900     18.910\n",
       "2010-09-01 04:00:00  15.8  1247.94000    3.6900     18.030\n",
       "...                   ...         ...       ...        ...\n",
       "2021-02-28 19:00:00  24.3   449.63625    5.4848    117.925\n",
       "2021-02-28 20:00:00  24.3   815.59975    5.4848     87.880\n",
       "2021-02-28 21:00:00  24.3   745.12325    5.4848     62.060\n",
       "2021-02-28 22:00:00  24.3   694.95150    5.4848     62.000\n",
       "2021-02-28 23:00:00  24.3   714.69630    5.4848     62.955\n",
       "\n",
       "[92016 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3f611db",
   "metadata": {},
   "outputs": [],
   "source": [
    "day_data = dataset.resample('D').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cca36cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3834, 4)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a78baeb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Electric Price')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=( 15 , 8 ))\n",
    "plt.plot(day_data.index, day_data['ElecPrice'], '--' , marker= '*' , )\n",
    "plt.grid()\n",
    "plt.xlabel( 'Year' )\n",
    "plt.ylabel( 'Electric Price' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "093422fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.004254\n",
      "p-value: 0.045027\n"
     ]
    }
   ],
   "source": [
    "#P-value of sysload and gas price to 6 d.p\n",
    "sys = adfuller(day_data['SysLoad'])\n",
    "gas = adfuller(day_data['GasPrice'])\n",
    "\n",
    "print(f'p-value: {round(sys[1],6)}')\n",
    "print(f'p-value: {round(gas[1],6)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "59f8c49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Initially reseting the index\n",
    "\n",
    "train_data = day_data.iloc[:2757,:]\n",
    "test_data = day_data.iloc[2757:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b8790d0a",
   "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>FullDate</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>SysLoad</th>\n",
       "      <th>GasPrice</th>\n",
       "      <th>ElecPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-09-01</td>\n",
       "      <td>379.2</td>\n",
       "      <td>41565.380</td>\n",
       "      <td>88.56</td>\n",
       "      <td>2751.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-09-02</td>\n",
       "      <td>369.6</td>\n",
       "      <td>38898.990</td>\n",
       "      <td>88.56</td>\n",
       "      <td>570.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-09-03</td>\n",
       "      <td>439.2</td>\n",
       "      <td>41665.760</td>\n",
       "      <td>106.80</td>\n",
       "      <td>504.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-09-04</td>\n",
       "      <td>328.8</td>\n",
       "      <td>35638.085</td>\n",
       "      <td>81.36</td>\n",
       "      <td>502.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-09-05</td>\n",
       "      <td>357.6</td>\n",
       "      <td>35611.410</td>\n",
       "      <td>106.80</td>\n",
       "      <td>500.555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FullDate   Tmax    SysLoad  GasPrice  ElecPrice\n",
       "0 2010-09-01  379.2  41565.380     88.56   2751.700\n",
       "1 2010-09-02  369.6  38898.990     88.56    570.810\n",
       "2 2010-09-03  439.2  41665.760    106.80    504.975\n",
       "3 2010-09-04  328.8  35638.085     81.36    502.275\n",
       "4 2010-09-05  357.6  35611.410    106.80    500.555"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.reset_index(inplace = True)\n",
    "train_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "246c184a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-09-01</td>\n",
       "      <td>2751.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-09-02</td>\n",
       "      <td>570.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-09-03</td>\n",
       "      <td>504.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-09-04</td>\n",
       "      <td>502.275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-09-05</td>\n",
       "      <td>500.555</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds         y\n",
       "0 2010-09-01  2751.700\n",
       "1 2010-09-02   570.810\n",
       "2 2010-09-03   504.975\n",
       "3 2010-09-04   502.275\n",
       "4 2010-09-05   500.555"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train = train_data.iloc[:,[0,-1]]\n",
    "data_train = data_train.rename(columns={\"FullDate\": \"ds\", \"ElecPrice\": \"y\"})\n",
    "data_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7a67ab07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.5672, -2.8626)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking for critical values\n",
    "elecprice = adfuller(data_train['y'])\n",
    "round(gas[4]['10%'],4), round(elecprice[4]['5%'],4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e60a1bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\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>FullDate</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>SysLoad</th>\n",
       "      <th>GasPrice</th>\n",
       "      <th>ElecPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-20</td>\n",
       "      <td>540.0</td>\n",
       "      <td>7696.91130</td>\n",
       "      <td>201.5880</td>\n",
       "      <td>1833.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>571.2</td>\n",
       "      <td>4658.05865</td>\n",
       "      <td>203.2560</td>\n",
       "      <td>1730.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>722.4</td>\n",
       "      <td>19161.78525</td>\n",
       "      <td>203.2560</td>\n",
       "      <td>2260.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>811.2</td>\n",
       "      <td>18090.43435</td>\n",
       "      <td>198.6456</td>\n",
       "      <td>2138.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-24</td>\n",
       "      <td>832.8</td>\n",
       "      <td>3763.53865</td>\n",
       "      <td>180.2160</td>\n",
       "      <td>1721.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FullDate   Tmax      SysLoad  GasPrice  ElecPrice\n",
       "0 2018-03-20  540.0   7696.91130  201.5880    1833.00\n",
       "1 2018-03-21  571.2   4658.05865  203.2560    1730.49\n",
       "2 2018-03-22  722.4  19161.78525  203.2560    2260.55\n",
       "3 2018-03-23  811.2  18090.43435  198.6456    2138.76\n",
       "4 2018-03-24  832.8   3763.53865  180.2160    1721.70"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.reset_index(inplace = True)\n",
    "test_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f17859b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-20</td>\n",
       "      <td>1833.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>1730.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>2260.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>2138.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-24</td>\n",
       "      <td>1721.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds        y\n",
       "0 2018-03-20  1833.00\n",
       "1 2018-03-21  1730.49\n",
       "2 2018-03-22  2260.55\n",
       "3 2018-03-23  2138.76\n",
       "4 2018-03-24  1721.70"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test = test_data.iloc[:,[0,-1]]\n",
    "data_test = data_test.rename(columns={\"FullDate\": \"ds\", \"ElecPrice\": \"y\"})\n",
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eebf408f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.iloc[:,[0,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca9e1911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-20</td>\n",
       "      <td>1833.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>1730.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>2260.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>2138.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-24</td>\n",
       "      <td>1721.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds        y\n",
       "0 2018-03-20  1833.00\n",
       "1 2018-03-21  1730.49\n",
       "2 2018-03-22  2260.55\n",
       "3 2018-03-23  2138.76\n",
       "4 2018-03-24  1721.70"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = test_data.rename(columns={\"FullDate\": \"ds\", \"ElecPrice\": \"y\"})\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e601e057",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14:50:08 - cmdstanpy - INFO - Chain [1] start processing\n",
      "14:50:08 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x287d9ec2770>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Prophet()\n",
    "model.fit(data_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9dab6b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>additive_terms</th>\n",
       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>yearly</th>\n",
       "      <th>yearly_lower</th>\n",
       "      <th>yearly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-20</td>\n",
       "      <td>2679.523236</td>\n",
       "      <td>174.458938</td>\n",
       "      <td>4901.866733</td>\n",
       "      <td>2679.523236</td>\n",
       "      <td>2679.523236</td>\n",
       "      <td>-154.882441</td>\n",
       "      <td>-154.882441</td>\n",
       "      <td>-154.882441</td>\n",
       "      <td>9.737801</td>\n",
       "      <td>9.737801</td>\n",
       "      <td>9.737801</td>\n",
       "      <td>-164.620241</td>\n",
       "      <td>-164.620241</td>\n",
       "      <td>-164.620241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2524.640796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-21</td>\n",
       "      <td>2680.298031</td>\n",
       "      <td>212.340162</td>\n",
       "      <td>5199.502232</td>\n",
       "      <td>2680.298031</td>\n",
       "      <td>2680.298031</td>\n",
       "      <td>-2.419802</td>\n",
       "      <td>-2.419802</td>\n",
       "      <td>-2.419802</td>\n",
       "      <td>159.363178</td>\n",
       "      <td>159.363178</td>\n",
       "      <td>159.363178</td>\n",
       "      <td>-161.782980</td>\n",
       "      <td>-161.782980</td>\n",
       "      <td>-161.782980</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2677.878229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-22</td>\n",
       "      <td>2681.072826</td>\n",
       "      <td>341.730408</td>\n",
       "      <td>5188.198958</td>\n",
       "      <td>2681.072826</td>\n",
       "      <td>2681.072826</td>\n",
       "      <td>95.746291</td>\n",
       "      <td>95.746291</td>\n",
       "      <td>95.746291</td>\n",
       "      <td>257.127937</td>\n",
       "      <td>257.127937</td>\n",
       "      <td>257.127937</td>\n",
       "      <td>-161.381646</td>\n",
       "      <td>-161.381646</td>\n",
       "      <td>-161.381646</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2776.819117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>2681.847620</td>\n",
       "      <td>252.597980</td>\n",
       "      <td>4944.276338</td>\n",
       "      <td>2681.847620</td>\n",
       "      <td>2681.847620</td>\n",
       "      <td>-112.972998</td>\n",
       "      <td>-112.972998</td>\n",
       "      <td>-112.972998</td>\n",
       "      <td>50.277336</td>\n",
       "      <td>50.277336</td>\n",
       "      <td>50.277336</td>\n",
       "      <td>-163.250334</td>\n",
       "      <td>-163.250334</td>\n",
       "      <td>-163.250334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2568.874622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-24</td>\n",
       "      <td>2682.622415</td>\n",
       "      <td>7.455192</td>\n",
       "      <td>4551.081934</td>\n",
       "      <td>2682.622415</td>\n",
       "      <td>2682.622415</td>\n",
       "      <td>-438.612511</td>\n",
       "      <td>-438.612511</td>\n",
       "      <td>-438.612511</td>\n",
       "      <td>-271.465795</td>\n",
       "      <td>-271.465795</td>\n",
       "      <td>-271.465795</td>\n",
       "      <td>-167.146716</td>\n",
       "      <td>-167.146716</td>\n",
       "      <td>-167.146716</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2244.009904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds        trend  yhat_lower   yhat_upper  trend_lower  trend_upper  \\\n",
       "0 2018-03-20  2679.523236  174.458938  4901.866733  2679.523236  2679.523236   \n",
       "1 2018-03-21  2680.298031  212.340162  5199.502232  2680.298031  2680.298031   \n",
       "2 2018-03-22  2681.072826  341.730408  5188.198958  2681.072826  2681.072826   \n",
       "3 2018-03-23  2681.847620  252.597980  4944.276338  2681.847620  2681.847620   \n",
       "4 2018-03-24  2682.622415    7.455192  4551.081934  2682.622415  2682.622415   \n",
       "\n",
       "   additive_terms  additive_terms_lower  additive_terms_upper      weekly  \\\n",
       "0     -154.882441           -154.882441           -154.882441    9.737801   \n",
       "1       -2.419802             -2.419802             -2.419802  159.363178   \n",
       "2       95.746291             95.746291             95.746291  257.127937   \n",
       "3     -112.972998           -112.972998           -112.972998   50.277336   \n",
       "4     -438.612511           -438.612511           -438.612511 -271.465795   \n",
       "\n",
       "   weekly_lower  weekly_upper      yearly  yearly_lower  yearly_upper  \\\n",
       "0      9.737801      9.737801 -164.620241   -164.620241   -164.620241   \n",
       "1    159.363178    159.363178 -161.782980   -161.782980   -161.782980   \n",
       "2    257.127937    257.127937 -161.381646   -161.381646   -161.381646   \n",
       "3     50.277336     50.277336 -163.250334   -163.250334   -163.250334   \n",
       "4   -271.465795   -271.465795 -167.146716   -167.146716   -167.146716   \n",
       "\n",
       "   multiplicative_terms  multiplicative_terms_lower  \\\n",
       "0                   0.0                         0.0   \n",
       "1                   0.0                         0.0   \n",
       "2                   0.0                         0.0   \n",
       "3                   0.0                         0.0   \n",
       "4                   0.0                         0.0   \n",
       "\n",
       "   multiplicative_terms_upper         yhat  \n",
       "0                         0.0  2524.640796  \n",
       "1                         0.0  2677.878229  \n",
       "2                         0.0  2776.819117  \n",
       "3                         0.0  2568.874622  \n",
       "4                         0.0  2244.009904  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = model.predict(data_test) \n",
    "forecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a29eb511",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Metric(y_true,y_pred):\n",
    "  y_true,y_pred = np.array(y_true), np.array(y_pred)\n",
    "  return np.mean(np.abs((y_true - y_pred)/y_true)) *100\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "244000e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mape(y, yhat):\n",
    "  y = y.reset_index(drop=True)\n",
    "  return round(np.mean(np.abs((y - yhat)/y)*100),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7b7a26a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "540.82"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#running evaluation on our model\n",
    "MAPE = Metric(test_data['y'],forecast['yhat'])\n",
    "round(MAPE,2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aea72f59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3200.06"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for RMSE\n",
    "\n",
    "def rmse(y, yhat):\n",
    "  y = y.reset_index(drop=True)\n",
    "  return round(np.sqrt(np.mean(np.square(y - yhat))),2)\n",
    "\n",
    "# evaluate rmse\n",
    "rmse(test_data['y'],forecast['yhat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e13e704a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x287db23e0e0>,\n",
       " <matplotlib.collections.PolyCollection at 0x287db1a1c30>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting to see month of yearly seasonality with highest value\n",
    "plot_yearly(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "961c20c7",
   "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>FullDate</th>\n",
       "      <th>Tmax</th>\n",
       "      <th>SysLoad</th>\n",
       "      <th>GasPrice</th>\n",
       "      <th>ElecPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-09-01 00:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1688.215</td>\n",
       "      <td>3.69</td>\n",
       "      <td>23.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-09-01 01:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1669.195</td>\n",
       "      <td>3.69</td>\n",
       "      <td>22.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-09-01 02:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1491.980</td>\n",
       "      <td>3.69</td>\n",
       "      <td>22.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-09-01 03:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1330.760</td>\n",
       "      <td>3.69</td>\n",
       "      <td>18.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-09-01 04:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1247.940</td>\n",
       "      <td>3.69</td>\n",
       "      <td>18.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             FullDate  Tmax   SysLoad  GasPrice  ElecPrice\n",
       "0 2010-09-01 00:00:00  15.8  1688.215      3.69     23.710\n",
       "1 2010-09-01 01:00:00  15.8  1669.195      3.69     22.635\n",
       "2 2010-09-01 02:00:00  15.8  1491.980      3.69     22.565\n",
       "3 2010-09-01 03:00:00  15.8  1330.760      3.69     18.910\n",
       "4 2010-09-01 04:00:00  15.8  1247.940      3.69     18.030"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.reset_index(inplace = True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e337c1db",
   "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>index</th>\n",
       "      <th>ds</th>\n",
       "      <th>add2</th>\n",
       "      <th>add1</th>\n",
       "      <th>add3</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2010-09-01 00:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1688.215</td>\n",
       "      <td>3.69</td>\n",
       "      <td>23.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-09-01 01:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1669.195</td>\n",
       "      <td>3.69</td>\n",
       "      <td>22.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2010-09-01 02:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1491.980</td>\n",
       "      <td>3.69</td>\n",
       "      <td>22.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2010-09-01 03:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1330.760</td>\n",
       "      <td>3.69</td>\n",
       "      <td>18.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2010-09-01 04:00:00</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1247.940</td>\n",
       "      <td>3.69</td>\n",
       "      <td>18.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                  ds  add2      add1  add3       y\n",
       "0      0 2010-09-01 00:00:00  15.8  1688.215  3.69  23.710\n",
       "1      1 2010-09-01 01:00:00  15.8  1669.195  3.69  22.635\n",
       "2      2 2010-09-01 02:00:00  15.8  1491.980  3.69  22.565\n",
       "3      3 2010-09-01 03:00:00  15.8  1330.760  3.69  18.910\n",
       "4      4 2010-09-01 04:00:00  15.8  1247.940  3.69  18.030"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = dataset.reset_index().rename(columns={\"FullDate\": \"ds\", \"ElecPrice\": \"y\", 'SysLoad': 'add1', 'Tmax': 'add2', 'GasPrice': 'add3'})\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f5030e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.reset_index(inplace= True)\n",
    "train_new = data.iloc[:2757,:]\n",
    "test_new = data.iloc[2757:,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ae94b8e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x287c7222c80>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2 = Prophet()\n",
    "model_2.add_regressor('add1')\n",
    "model_2.add_regressor('add2')\n",
    "model_2.add_regressor('add3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f9370a3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14:50:09 - cmdstanpy - INFO - Chain [1] start processing\n",
      "14:50:10 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    }
   ],
   "source": [
    "#training new model\n",
    "model_21 = model_2.fit(train_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e1ae0060",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_forecast = model_21.predict(test_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "06cc49f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#evaluating MAPE of new model\n",
    "MAPE_2 = mape(test_new['y'],new_forecast['yhat'])\n",
    "round(MAPE_2,2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "672922d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1331.71"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating new RMSE\n",
    "RMSE_3 = np.sqrt(metrics.mean_squared_error(test_new['y'],new_forecast['yhat']))\n",
    "round(RMSE_3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a9c898e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting to see daily seasonality with the highest value\n",
    "model.plot_components(new_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fac25401",
   "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>ds</th>\n",
       "      <th>add1</th>\n",
       "      <th>add2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-09-01 00:00:00</td>\n",
       "      <td>1688.215</td>\n",
       "      <td>3.69</td>\n",
       "      <td>23.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-09-01 01:00:00</td>\n",
       "      <td>1669.195</td>\n",
       "      <td>3.69</td>\n",
       "      <td>22.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-09-01 02:00:00</td>\n",
       "      <td>1491.980</td>\n",
       "      <td>3.69</td>\n",
       "      <td>22.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-09-01 03:00:00</td>\n",
       "      <td>1330.760</td>\n",
       "      <td>3.69</td>\n",
       "      <td>18.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-09-01 04:00:00</td>\n",
       "      <td>1247.940</td>\n",
       "      <td>3.69</td>\n",
       "      <td>18.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   ds      add1  add2       y\n",
       "0 2010-09-01 00:00:00  1688.215  3.69  23.710\n",
       "1 2010-09-01 01:00:00  1669.195  3.69  22.635\n",
       "2 2010-09-01 02:00:00  1491.980  3.69  22.565\n",
       "3 2010-09-01 03:00:00  1330.760  3.69  18.910\n",
       "4 2010-09-01 04:00:00  1247.940  3.69  18.030"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#initialising a new dataset to train a multivariate time series excluding T-max\n",
    "new_data = dataset.rename(columns = {'FullDate':'ds','ElecPrice':'y','SysLoad':'add1','GasPrice':'add2'})\n",
    "new_data.drop('Tmax', axis=1,inplace=True)\n",
    "\n",
    "new_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3c3e702e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the data\n",
    "train1 = new_data[:2757]\n",
    "test1 = new_data[2757:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9fdd7093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x287e21162c0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model\n",
    "model1 = Prophet()\n",
    "model1.add_regressor('add1')\n",
    "model1.add_regressor('add2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6f88c706",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "14:50:34 - cmdstanpy - INFO - Chain [1] start processing\n",
      "14:50:35 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    }
   ],
   "source": [
    "model1.fit(train1) \n",
    "forecast1 = model1.predict(test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f4577ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\godso\\AppData\\Local\\Temp\\ipykernel_7684\\3617687093.py:3: RuntimeWarning: divide by zero encountered in divide\n",
      "  return np.mean(np.abs((y_true - y_pred)/y_true)) *100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "inf"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting the MAPE for the new model \n",
    "MAPE_4 = Metric(test1['y'],forecast1['yhat'])\n",
    "round(MAPE_4,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95029e5e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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