Exponential_Smoothing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential Smoothing Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-01-2012</td>\n",
       "      <td>58.485714</td>\n",
       "      <td>58.928570</td>\n",
       "      <td>58.428570</td>\n",
       "      <td>58.747143</td>\n",
       "      <td>50.765709</td>\n",
       "      <td>75555200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04-01-2012</td>\n",
       "      <td>58.571430</td>\n",
       "      <td>59.240002</td>\n",
       "      <td>58.468571</td>\n",
       "      <td>59.062859</td>\n",
       "      <td>51.038536</td>\n",
       "      <td>65005500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05-01-2012</td>\n",
       "      <td>59.278572</td>\n",
       "      <td>59.792858</td>\n",
       "      <td>58.952858</td>\n",
       "      <td>59.718571</td>\n",
       "      <td>51.605175</td>\n",
       "      <td>67817400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06-01-2012</td>\n",
       "      <td>59.967144</td>\n",
       "      <td>60.392857</td>\n",
       "      <td>59.888573</td>\n",
       "      <td>60.342857</td>\n",
       "      <td>52.144630</td>\n",
       "      <td>79573200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09-01-2012</td>\n",
       "      <td>60.785713</td>\n",
       "      <td>61.107143</td>\n",
       "      <td>60.192856</td>\n",
       "      <td>60.247143</td>\n",
       "      <td>52.061932</td>\n",
       "      <td>98506100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>23-12-2019</td>\n",
       "      <td>280.529999</td>\n",
       "      <td>284.250000</td>\n",
       "      <td>280.369995</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>282.054138</td>\n",
       "      <td>24643000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>24-12-2019</td>\n",
       "      <td>284.690002</td>\n",
       "      <td>284.890015</td>\n",
       "      <td>282.920013</td>\n",
       "      <td>284.269989</td>\n",
       "      <td>282.322266</td>\n",
       "      <td>12119700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>26-12-2019</td>\n",
       "      <td>284.820007</td>\n",
       "      <td>289.980011</td>\n",
       "      <td>284.700012</td>\n",
       "      <td>289.910004</td>\n",
       "      <td>287.923645</td>\n",
       "      <td>23280300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>27-12-2019</td>\n",
       "      <td>291.119995</td>\n",
       "      <td>293.970001</td>\n",
       "      <td>288.119995</td>\n",
       "      <td>289.799988</td>\n",
       "      <td>287.814392</td>\n",
       "      <td>36566500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>30-12-2019</td>\n",
       "      <td>289.459991</td>\n",
       "      <td>292.690002</td>\n",
       "      <td>285.220001</td>\n",
       "      <td>291.519989</td>\n",
       "      <td>289.522614</td>\n",
       "      <td>36028600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     03-01-2012   58.485714   58.928570   58.428570   58.747143   50.765709   \n",
       "1     04-01-2012   58.571430   59.240002   58.468571   59.062859   51.038536   \n",
       "2     05-01-2012   59.278572   59.792858   58.952858   59.718571   51.605175   \n",
       "3     06-01-2012   59.967144   60.392857   59.888573   60.342857   52.144630   \n",
       "4     09-01-2012   60.785713   61.107143   60.192856   60.247143   52.061932   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "2006  23-12-2019  280.529999  284.250000  280.369995  284.000000  282.054138   \n",
       "2007  24-12-2019  284.690002  284.890015  282.920013  284.269989  282.322266   \n",
       "2008  26-12-2019  284.820007  289.980011  284.700012  289.910004  287.923645   \n",
       "2009  27-12-2019  291.119995  293.970001  288.119995  289.799988  287.814392   \n",
       "2010  30-12-2019  289.459991  292.690002  285.220001  291.519989  289.522614   \n",
       "\n",
       "        Volume  \n",
       "0     75555200  \n",
       "1     65005500  \n",
       "2     67817400  \n",
       "3     79573200  \n",
       "4     98506100  \n",
       "...        ...  \n",
       "2006  24643000  \n",
       "2007  12119700  \n",
       "2008  23280300  \n",
       "2009  36566500  \n",
       "2010  36028600  \n",
       "\n",
       "[2011 rows x 7 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/AAPL.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>58.485714</td>\n",
       "      <td>58.928570</td>\n",
       "      <td>58.428570</td>\n",
       "      <td>58.747143</td>\n",
       "      <td>50.765709</td>\n",
       "      <td>75555200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>58.571430</td>\n",
       "      <td>59.240002</td>\n",
       "      <td>58.468571</td>\n",
       "      <td>59.062859</td>\n",
       "      <td>51.038536</td>\n",
       "      <td>65005500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>59.278572</td>\n",
       "      <td>59.792858</td>\n",
       "      <td>58.952858</td>\n",
       "      <td>59.718571</td>\n",
       "      <td>51.605175</td>\n",
       "      <td>67817400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>59.967144</td>\n",
       "      <td>60.392857</td>\n",
       "      <td>59.888573</td>\n",
       "      <td>60.342857</td>\n",
       "      <td>52.144630</td>\n",
       "      <td>79573200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>60.785713</td>\n",
       "      <td>61.107143</td>\n",
       "      <td>60.192856</td>\n",
       "      <td>60.247143</td>\n",
       "      <td>52.061932</td>\n",
       "      <td>98506100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date       Open       High        Low      Close  Adj Close    Volume\n",
       "0 2012-01-03  58.485714  58.928570  58.428570  58.747143  50.765709  75555200\n",
       "1 2012-01-04  58.571430  59.240002  58.468571  59.062859  51.038536  65005500\n",
       "2 2012-01-05  59.278572  59.792858  58.952858  59.718571  51.605175  67817400\n",
       "3 2012-01-06  59.967144  60.392857  59.888573  60.342857  52.144630  79573200\n",
       "4 2012-01-09  60.785713  61.107143  60.192856  60.247143  52.061932  98506100"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting Date Column to DateTime Format\n",
    "df['Date'] = pd.to_datetime(df['Date'], format='%d-%m-%Y')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>58.485714</td>\n",
       "      <td>58.928570</td>\n",
       "      <td>58.428570</td>\n",
       "      <td>58.747143</td>\n",
       "      <td>50.765709</td>\n",
       "      <td>75555200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>58.571430</td>\n",
       "      <td>59.240002</td>\n",
       "      <td>58.468571</td>\n",
       "      <td>59.062859</td>\n",
       "      <td>51.038536</td>\n",
       "      <td>65005500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>59.278572</td>\n",
       "      <td>59.792858</td>\n",
       "      <td>58.952858</td>\n",
       "      <td>59.718571</td>\n",
       "      <td>51.605175</td>\n",
       "      <td>67817400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>59.967144</td>\n",
       "      <td>60.392857</td>\n",
       "      <td>59.888573</td>\n",
       "      <td>60.342857</td>\n",
       "      <td>52.144630</td>\n",
       "      <td>79573200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>60.785713</td>\n",
       "      <td>61.107143</td>\n",
       "      <td>60.192856</td>\n",
       "      <td>60.247143</td>\n",
       "      <td>52.061932</td>\n",
       "      <td>98506100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-23</th>\n",
       "      <td>280.529999</td>\n",
       "      <td>284.250000</td>\n",
       "      <td>280.369995</td>\n",
       "      <td>284.000000</td>\n",
       "      <td>282.054138</td>\n",
       "      <td>24643000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-24</th>\n",
       "      <td>284.690002</td>\n",
       "      <td>284.890015</td>\n",
       "      <td>282.920013</td>\n",
       "      <td>284.269989</td>\n",
       "      <td>282.322266</td>\n",
       "      <td>12119700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-26</th>\n",
       "      <td>284.820007</td>\n",
       "      <td>289.980011</td>\n",
       "      <td>284.700012</td>\n",
       "      <td>289.910004</td>\n",
       "      <td>287.923645</td>\n",
       "      <td>23280300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>291.119995</td>\n",
       "      <td>293.970001</td>\n",
       "      <td>288.119995</td>\n",
       "      <td>289.799988</td>\n",
       "      <td>287.814392</td>\n",
       "      <td>36566500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>289.459991</td>\n",
       "      <td>292.690002</td>\n",
       "      <td>285.220001</td>\n",
       "      <td>291.519989</td>\n",
       "      <td>289.522614</td>\n",
       "      <td>36028600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "2012-01-03   58.485714   58.928570   58.428570   58.747143   50.765709   \n",
       "2012-01-04   58.571430   59.240002   58.468571   59.062859   51.038536   \n",
       "2012-01-05   59.278572   59.792858   58.952858   59.718571   51.605175   \n",
       "2012-01-06   59.967144   60.392857   59.888573   60.342857   52.144630   \n",
       "2012-01-09   60.785713   61.107143   60.192856   60.247143   52.061932   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2019-12-23  280.529999  284.250000  280.369995  284.000000  282.054138   \n",
       "2019-12-24  284.690002  284.890015  282.920013  284.269989  282.322266   \n",
       "2019-12-26  284.820007  289.980011  284.700012  289.910004  287.923645   \n",
       "2019-12-27  291.119995  293.970001  288.119995  289.799988  287.814392   \n",
       "2019-12-30  289.459991  292.690002  285.220001  291.519989  289.522614   \n",
       "\n",
       "              Volume  \n",
       "2012-01-03  75555200  \n",
       "2012-01-04  65005500  \n",
       "2012-01-05  67817400  \n",
       "2012-01-06  79573200  \n",
       "2012-01-09  98506100  \n",
       "...              ...  \n",
       "2019-12-23  24643000  \n",
       "2019-12-24  12119700  \n",
       "2019-12-26  23280300  \n",
       "2019-12-27  36566500  \n",
       "2019-12-30  36028600  \n",
       "\n",
       "[2011 rows x 6 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting Datetime as Index\n",
    "dt = df.set_index('Date').rename_axis(None)\n",
    "dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the Dataset\n",
    "data = dt[['Close']]\n",
    "train_size = int(len(data) * 0.80) # 80% split\n",
    "\n",
    "train, test = data.iloc[:train_size], data.iloc[train_size:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Exponential Smoothing Model\n",
    "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
    "\n",
    "# Holt-Winters Seasonal Model with additive trend and seasonality\n",
    "model = ExponentialSmoothing(\n",
    "    train,\n",
    "    seasonal='add',\n",
    "    trend='add',\n",
    "    seasonal_periods=252\n",
    ")\n",
    "\n",
    "fitted_model = model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = fitted_model.forecast(len(test))\n",
    "forecast_df = pd.DataFrame(forecast.values, index=test.index, columns=['Forecast'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 19.698581347357223\n",
      "Mean Squared Error: 646.4299918709464\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "mae = mean_absolute_error(test['Close'], forecast_df['Forecast'])\n",
    "mse = mean_squared_error(test['Close'], forecast_df['Forecast'])\n",
    "\n",
    "print(f'Mean Absolute Error: {mae}')\n",
    "print(f'Mean Squared Error: {mse}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the actual data, test and predicted data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train, label='Train')\n",
    "plt.plot(test, label='Test')\n",
    "plt.plot(forecast_df, label='Forecast')\n",
    "plt.title('Price Forecast using Exponential Smoothing')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Holt-Winters Seasonal Model with additive trend and seasonality\n",
    "model = ExponentialSmoothing(\n",
    "    data,\n",
    "    seasonal='add',\n",
    "    trend='add',\n",
    "    seasonal_periods=252\n",
    ")\n",
    "\n",
    "fitted_model = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making Forecast for 30 days\n",
    "forecast_steps = 30\n",
    "forecast = fitted_model.forecast(forecast_steps)\n",
    "\n",
    "forecast_dates = pd.date_range(\n",
    "    start = data.index[-1] + pd.Timedelta(days=1),\n",
    "    periods=forecast_steps,\n",
    "    freq='D'\n",
    ")\n",
    "forecast_df = pd.DataFrame(\n",
    "    forecast.values,\n",
    "    index=forecast_dates,\n",
    "    columns=['Forecast']\n",
    ")\n",
    "\n",
    "# Plotting Forecast\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data, label='Actual Price')\n",
    "plt.plot(forecast_df, label='Forecast')\n",
    "plt.title('Price Forecast using Exponential Smoothing')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = data.iloc[:train_size], data.iloc[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining Grid Parameters\n",
    "\n",
    "param_grid = {\n",
    "    'trend' : ['add', 'mul', None],\n",
    "    'seasonal' : ['add', 'mul', None],\n",
    "    'seasonal_periods' : [7, 30, 90, 252] # weekly, monthly, quartly, yearly seasonality\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Parameters: {'trend': 'mul', 'seasonal': 'mul', 'seasonal_periods': 252}\n",
      "Best MAE: 18.936177462722302\n"
     ]
    }
   ],
   "source": [
    "best_mae = float('inf')\n",
    "best_params = {}\n",
    "\n",
    "# Performing grid search\n",
    "for trend in param_grid['trend']:\n",
    "    for seasonal in param_grid['seasonal']:\n",
    "        for seasonal_periods in param_grid['seasonal_periods']:\n",
    "            try:\n",
    "                model = ExponentialSmoothing(\n",
    "                    train,\n",
    "                    trend=trend,\n",
    "                    seasonal=seasonal,\n",
    "                    seasonal_periods=seasonal_periods\n",
    "                )\n",
    "                fit = model.fit()\n",
    "                forecast = fit.forecast(len(test))\n",
    "                mae = mean_absolute_error(test['Close'], forecast)\n",
    "                if mae < best_mae:\n",
    "                    best_mae = mae\n",
    "                    best_params = {\n",
    "                        'trend': trend,\n",
    "                        'seasonal': seasonal,\n",
    "                        'seasonal_periods': seasonal_periods\n",
    "                    }\n",
    "            except:\n",
    "                continue\n",
    "\n",
    "print(f'Best Parameters: {best_params}')\n",
    "print(f'Best MAE: {best_mae}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building Model using Best Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting using best parameters\n",
    "final_model = ExponentialSmoothing(\n",
    "    train,\n",
    "    trend = best_params['trend'],\n",
    "    seasonal = best_params['seasonal'],\n",
    "    seasonal_periods= best_params['seasonal_periods']\n",
    ")\n",
    "best_fit = final_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forecasting data\n",
    "forecast = best_fit.forecast(len(test))\n",
    "forecast_df = pd.DataFrame(forecast.values, index=test.index, columns=['Forecast'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Forecasted values\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train, label='train')\n",
    "plt.plot(test, label='test')\n",
    "plt.plot(forecast_df, label='forecast')\n",
    "plt.title('Price Forecast using Tuned Exponential Smoothing')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting using best parameters\n",
    "final_model = ExponentialSmoothing(\n",
    "    data,\n",
    "    trend = best_params['trend'],\n",
    "    seasonal = best_params['seasonal'],\n",
    "    seasonal_periods= best_params['seasonal_periods']\n",
    ")\n",
    "final_fit = final_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Forecasting data\n",
    "forecast_steps = 30\n",
    "forecast = final_fit.forecast(forecast_steps)\n",
    "forecast_dates = pd.date_range(\n",
    "    start = data.index[-1] + pd.Timedelta(days=1),\n",
    "    periods = forecast_steps,\n",
    "    freq='D'\n",
    ")\n",
    "forecast_df = pd.DataFrame(forecast.values, index=forecast_dates, columns=['Forecast'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Forecast\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(data, label='Actual Price')\n",
    "plt.plot(forecast_df, label='Forecast')\n",
    "plt.title('Forecast for 30 days')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TimeSeries_Analysis",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}