ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from ARIMA import rmse,mape,UStatistic\n",
    "import time\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    26498.0\n",
       "1    25147.0\n",
       "2    24574.0\n",
       "3    24393.0\n",
       "4    24860.0\n",
       "Name: demand, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('./PJME_hourly.csv')\n",
    "dataset['Datetime'] = pd.to_datetime(dataset[\"Datetime\"], format='%Y-%m-%d %H:%M:%S')\n",
    "dataset.rename(columns={'PJME_MW': 'demand'}, inplace=True)\n",
    "\n",
    "days2predict = 48\n",
    "train_size = int(len(dataset[\"demand\"]) - days2predict)\n",
    "train = dataset[:train_size].demand\n",
    "test = dataset[train_size:].demand\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cesar/.local/lib/python3.10/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "463.9061872959137\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cesar/.local/lib/python3.10/site-packages/statsmodels/base/model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "Error_df = pd.DataFrame(columns = [\"Name\",\"RMSE\",\"SMAPE\",\"Theils U\"])\n",
    "\n",
    "begin=time.time()\n",
    "\n",
    "\n",
    "config_arima = (14,1,4) # P=14, D=1 e Q=4 -> (p,d,q)\n",
    "arima_train = ARIMA(train,order=config_arima).fit()\n",
    "arima_test = ARIMA(test,order=config_arima).fit(arima_train.params)\n",
    "y_hat = arima_train.forecast(len(test))\n",
    "\n",
    "print(time.time() - begin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6a1b9a9b10>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(test)\n",
    "plt.plot(arima_test.predict(typ='levels'))\n",
    "plt.title('ARIMA Model')\n",
    "plt.legend(['Train Set','Test Set','ARIMA Model'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_284409/2160134833.py:9: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  Error_df = Error_df.append(dictResult,ignore_index=True)\n"
     ]
    },
    {
     "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>Name</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>Theils U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARMA 14,1,4</td>\n",
       "      <td>2177.5289</td>\n",
       "      <td>4.53%</td>\n",
       "      <td>1.750482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name       RMSE  SMAPE  Theils U\n",
       "0  ARMA 14,1,4  2177.5289  4.53%  1.750482"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAPE = mape(test.to_numpy(),y_hat.to_numpy())\n",
    "RMSE = rmse(test.to_numpy(),y_hat.to_numpy())\n",
    "U = UStatistic(test.to_numpy(),y_hat.to_numpy())\n",
    "dictResult= {   'Name': 'ARMA {},{},{}'.format(config_arima[0],config_arima[1],config_arima[2]),\n",
    "                'RMSE': RMSE,\n",
    "                'SMAPE': MAPE,\n",
    "                'Theils U': U,\n",
    "                }\n",
    "Error_df = Error_df.append(dictResult,ignore_index=True)\n",
    "Error_df.head()\n",
    "# 'RMSE': '3025.9366', 'SMAPE': '6.73%'"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}