ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARIMA Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>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": 130,
     "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": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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": 131,
     "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": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting Datetime as Index\n",
    "dt = df.set_index('Date').rename_axis(None)\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-03</th>\n",
       "      <td>-1.344666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-04</th>\n",
       "      <td>-1.338422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-05</th>\n",
       "      <td>-1.325455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-06</th>\n",
       "      <td>-1.313109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-09</th>\n",
       "      <td>-1.315001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-23</th>\n",
       "      <td>3.109983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-24</th>\n",
       "      <td>3.115323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-26</th>\n",
       "      <td>3.226861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>3.224685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>3.258700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2011 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Close\n",
       "2012-01-03 -1.344666\n",
       "2012-01-04 -1.338422\n",
       "2012-01-05 -1.325455\n",
       "2012-01-06 -1.313109\n",
       "2012-01-09 -1.315001\n",
       "...              ...\n",
       "2019-12-23  3.109983\n",
       "2019-12-24  3.115323\n",
       "2019-12-26  3.226861\n",
       "2019-12-27  3.224685\n",
       "2019-12-30  3.258700\n",
       "\n",
       "[2011 rows x 1 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardizing the Data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Initializing Standard Scaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Applying Standard Scaler to only necessary features\n",
    "scaled_data = scaler.fit_transform(dt[['Close']])\n",
    "scaled_df = pd.DataFrame(scaled_data, columns=['Close'])\n",
    "scaled_df.index = dt.index\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardizing data ensures that the data is on consistent scale, improving the performance and reliability of various algorithms, enhancing interpretability, and preventing numerical issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ARIMA\n",
    "\n",
    "An ARIMA model is characterized by 3 terms (p, d, q):\n",
    "\n",
    "* p is the order of the AR term. It refers to the number of lags to be used as predictors.\n",
    "* d is the number of differencing required to make the time series stationary\n",
    "* q is the order of the MA term. It refers to the number of lagged forecast errors that should go into the ARIMA model.\n",
    "\n",
    "As seen per the parameters required by the model, any stationary time series can be modeled with ARIMA models.\n",
    "Therefore we have to check if our dataset have stationarity or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stationarity\n",
    "\n",
    "Subtract the previous value from the current value. Now if we just difference once, we might not get a stationary series so we might need to do that multiple times.\n",
    "\n",
    "And the minimum number of differencing operations needed to make the series stationary needs to be imputed into our ARIMA model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ADF test\n",
    "\n",
    "We will use the Augumented Dickey Fuller (ADF) test to check if the price series is stationary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hypothesis Test: To evaluate Stationarity\n",
    "\n",
    "Null Hypothesis: Time series is non-stationary.\\\n",
    "Alternative Hypothesis: Time series is Stationary.\n",
    "\n",
    "So, if the p-value of the test is less than the significance level of 0.05,\n",
    "then we can reject the null hypothesis and infer that the time series is indeed stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Statistic: 1.2193701254377973\n",
      "p-value: 0.9961170178032009\n",
      "\n",
      "Accept Null Hypothesis: Data is non-stationary\n"
     ]
    }
   ],
   "source": [
    "# Hypothesis Test\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "result = adfuller(scaled_df)\n",
    "print(f'ADF Statistic: {result[0]}')\n",
    "print(f'p-value: {result[1]}')\n",
    "\n",
    "if result[1] > 0.05:\n",
    "    print(\"\\nAccept Null Hypothesis: Data is non-stationary\")\n",
    "else:\n",
    "    print(\"\\nReject Null Hypothesis: Data is stationary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, in our case, te p-value is > 0.05,\\\n",
    "we will need to find the order of differencing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting Order of Differencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Differencing (d) = 1\n"
     ]
    }
   ],
   "source": [
    "from pmdarima.arima.utils import ndiffs\n",
    "\n",
    "print('Differencing (d) =', ndiffs(scaled_df, test='adf'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting the order of AutoRegressive Model (q)\n",
    "\n",
    "q is the order of the AR term. It refers to the number of lags to be used as predictors.\n",
    "\n",
    "We can find out the required number of AR terms by inspecting the Partial Autocorrelation (PACF) plot.\n",
    "\n",
    "The partial auto correlation represents the correlation between the series and its lags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "\n",
    "diff = scaled_df.diff().dropna()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,4))\n",
    "\n",
    "ax1.plot(diff)\n",
    "ax1.set_title('Differnce once')\n",
    "ax2.set_ylim(0,1)\n",
    "plot_pacf(diff, ax=ax2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the PACF lag (q) 2 is significant as it is above the significance line.\n",
    "\n",
    "Therefore, we will consider q = 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting order of Moving Average.\n",
    "\n",
    "p is the order of the Moving Average (MA) term. It refers to the number of lagged forecast errors that should go into the ARIMA model.\n",
    "\n",
    "We can look at the ACF plot for the number of MA terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "diff = scaled_df.diff().dropna()\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,4))\n",
    "\n",
    "ax1.plot(diff)\n",
    "ax1.set_title('Difference once')\n",
    "ax2.set_ylim(0,1)\n",
    "plot_acf(diff, ax=ax2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ACF lag (p) 1 is significant as it is just at the significance line.\n",
    "\n",
    "So, we will consider p = 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset\n",
    "train_size = int(len(scaled_df) * 0.80) # 80% split\n",
    "\n",
    "train, test = scaled_df.iloc[:train_size], scaled_df[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Close</td>      <th>  No. Observations:  </th>   <td>1608</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 1, 2)</td>  <th>  Log Likelihood     </th> <td>3221.232</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 03 Jul 2024</td> <th>  AIC                </th> <td>-6434.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:49:09</td>     <th>  BIC                </th> <td>-6412.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>-6426.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 1608</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.1432</td> <td>    0.875</td> <td>   -0.164</td> <td> 0.870</td> <td>   -1.858</td> <td>    1.572</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>    0.1858</td> <td>    0.875</td> <td>    0.212</td> <td> 0.832</td> <td>   -1.530</td> <td>    1.901</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.0173</td> <td>    0.043</td> <td>   -0.404</td> <td> 0.686</td> <td>   -0.101</td> <td>    0.067</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0011</td> <td> 2.25e-05</td> <td>   47.181</td> <td> 0.000</td> <td>    0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>963.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.94</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.80</td> <th>  Skew:              </th>  <td>-0.03</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>6.79</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &      Close       & \\textbf{  No. Observations:  } &    1608     \\\\\n",
       "\\textbf{Model:}                  &  ARIMA(1, 1, 2)  & \\textbf{  Log Likelihood     } &  3221.232   \\\\\n",
       "\\textbf{Date:}                   & Wed, 03 Jul 2024 & \\textbf{  AIC                } & -6434.465   \\\\\n",
       "\\textbf{Time:}                   &     19:49:09     & \\textbf{  BIC                } & -6412.936   \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } & -6426.472   \\\\\n",
       "\\textbf{}                        &      - 1608      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ar.L1}  &      -0.1432  &        0.875     &    -0.164  &         0.870        &       -1.858    &        1.572     \\\\\n",
       "\\textbf{ma.L1}  &       0.1858  &        0.875     &     0.212  &         0.832        &       -1.530    &        1.901     \\\\\n",
       "\\textbf{ma.L2}  &      -0.0173  &        0.043     &    -0.404  &         0.686        &       -0.101    &        0.067     \\\\\n",
       "\\textbf{sigma2} &       0.0011  &     2.25e-05     &    47.181  &         0.000        &        0.001    &        0.001     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.01 & \\textbf{  Jarque-Bera (JB):  } & 963.05  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.94 & \\textbf{  Prob(JB):          } &  0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 1.80 & \\textbf{  Skew:              } & -0.03   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &  6.79   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Close   No. Observations:                 1608\n",
       "Model:                 ARIMA(1, 1, 2)   Log Likelihood                3221.232\n",
       "Date:                Wed, 03 Jul 2024   AIC                          -6434.465\n",
       "Time:                        19:49:09   BIC                          -6412.936\n",
       "Sample:                             0   HQIC                         -6426.472\n",
       "                               - 1608                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.1432      0.875     -0.164      0.870      -1.858       1.572\n",
       "ma.L1          0.1858      0.875      0.212      0.832      -1.530       1.901\n",
       "ma.L2         -0.0173      0.043     -0.404      0.686      -0.101       0.067\n",
       "sigma2         0.0011   2.25e-05     47.181      0.000       0.001       0.001\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):               963.05\n",
       "Prob(Q):                              0.94   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.80   Skew:                            -0.03\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         6.79\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "model = ARIMA(train, order=(1,1,2)) # order=(p,d,q)\n",
    "model_fit = model.fit()\n",
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model_fit.forecast(steps=int(len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 0.5021438811989334\n",
      "Mean Squared Error: 0.4531569809432877\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "mae = mean_absolute_error(test, predictions)\n",
    "mse = mean_squared_error(test, predictions)\n",
    "\n",
    "print(f'Mean Absolute Error: {mae}')\n",
    "print(f'Mean Squared Error: {mse}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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": [
    "forecast = model_fit.forecast(steps=int(len(test)))\n",
    "forecast_values = scaler.inverse_transform(forecast.values.reshape(-1,1))\n",
    "forecasts_df = pd.DataFrame(forecast_values, index=test.index, columns=['Forecast'])\n",
    "\n",
    "original_train = dt.iloc[:train_size]\n",
    "original_test = dt.iloc[train_size:]\n",
    "\n",
    "\n",
    "# Plotting forecast\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(original_train.index, original_train['Close'], label='Train')\n",
    "plt.plot(original_test.index, original_test['Close'], label='Test')\n",
    "plt.plot(forecasts_df.index, forecasts_df['Forecast'], label='Forecast')\n",
    "plt.title('Forecast')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the residual to ensure they behave like white noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GI71uKw0C1aI42XF3Zi7L/VFsS8vKuefNBTkTdGdiNNNsWR0zwm+kRjUSV3+1eQeXPfRRtaCvtKy8WjD72JQlfLJ0Q1L73LazjLWb4wdlxTEHntfQePXyxz8GKoLA42+fxLBxr3Lz83P4YOFa3vx0VY15qNrE/WcPT+P1uatYtHZLMkVg284y3k+yGXFtfZRfm7uSv73+Gbe99GlS20tWOvdHtu0swx3uifMbFgmoa2o6m87PWdWa2pUbtvFJCqNnr9m0nb+/8Vn0fY+O+lukpr8iIiJSXaMKVCMSXSguWrOFhz+sPI1CvMCwPvzr3YUALFwTzJ25bWdw8Va1VgLg4Q8X840/vcWkT1fxxZrNLE7yoj6Tvvm3d7j5+TkAPPTBIu55s3rTz4cnL+bGCbO5e1LqzUI/XLg2OvJsphx96xsZ3d68FRt56IPg+Lnv7c+ZvzK1fppjH/+Yx6vUhpWXO/NWBNuJBKF/fOlT/jdtGU9MrZz2kHGvcsCNQXP0HaXlzPmyIkBdtm4rHy9ZV+P+v/fPDxM2Z4+t9VqxYXv0Bkqs2EAmNggsK3ealhQH+aqltuzGCbPjBpDJBndXPTmDb49/L6nvQG01quu3Bi0vslXDV1PQuG1nGR8tqt7E+uEPFydcd2fYlLZqE1uouLkQb9Tfl2atYPn6rZWWlZd7tRshG7aVculDH3HpQx8BMPyW1zkhnI948/ZSDh33Ku8vWMMHn6/l3c+q3yx4be4qbnpuDh8vWc+8FRujx/PazTvY45rneCw89ncmOI5ERESkcWlUgWrkEi3Rpc+YO9/iisdnUFbu/Pu9L9jjmueSbvKYaZGLuJIwUo7UQm4vLa92Ufl5GMzOWLqeI//wOoff/Fo95jQw5Yuv+OvrnwEw9okZ3DhhdrU0X4W1dZHa4VScdte7HH/7pPQyWUWmRxcdecebjH1iBuXlzvXPzGLMX4KLeHfndxNn8+mKxIF2ebnz0IeL+fmjlQcqGnXHmyxdF3zei9Zuoc/YCTzz8TIAWjUtqZR25cbtrNsSBFe/fvoTRt5e0eT3kHGvctJf3mbZuq2889nquHl4Jwwulq7bWq1ms+q34Bt/qtj2tp1lXPO/GXwZU0NdtVl10/AGS219re99+3O27ixj8sK1PDVtady2qgtWbWLLjvhNiD9atA5ILrisren39vDmUNM4N4dizVq2gT2ufo5l67bWmK4mp/71bQZe81z0+bVPfcIpf32nUsC9aXspv356JgAdWzVlxpLKtZmRGwVN4kyWGg6wS2l5OeXlzoMfLGLz9lLcnR/8azKn3fUuHy9Zx9PTg2Pr98/PYe8qozCv37qTp6Yt46lpy5jz5Qa27Kj4Hs9Yup6l67Zyy4tz+dbf3+WMu99LWNYxd77NsbdNio76C8GNlV+Ex34k4AZ4o5YaeBERESlcjSpQjXXlEzOq1Z5+FV7kT164lmuf+oQdpamP7Lt8/dZqTQ8Xr90Srf16ZfYK+oydUK2J5ZKvtvDDf09m644yysud6eFFaEl40RnbXHbELa9XWrd9y6YA1bb51rzVcWs2G0okSGlaXJxU+qenL2uQ2uFE3p6/mj5jJzB7efymtJGbC2VhALR5RxnL129l0dotjJ+0gB/9Z0ql9LOXb4jWPCUK3ufGBLeRQYwiwWib5iV8snR9tZpVqAg6qxp5+yTOvPv9uK9126U5AIeOe5U/vDi3ctmqBHUrY2pUn5m+jP+8t4gbYvpNVr0H0LQkiDjjNet84P3K38PN28v4v7ve5dKHplVavnrTdtydo259g+/988O4ZYgEsCVJNIWobSTtJV8FgWfzpjUfr/96dyE7ysp5be7KWveZyNRF6yoF8R+H3//YfvCbYh6v3rSDE//yFh8t+orHpizhs1WbosffrOUbWLSm8vcmUqN66UPT2P2qiVz5xAxueHZWdJ9LvtrKSX95m0seDGpL7327etPir2L66sbeBDnmj29Et9OsJLnvNsS/abHH1c9VOkbOvS/+5yyVmdlIM5trZvPNbGyc139pZtPCv0/MrMzMOoSvLTSzGeFrmv9MRERyRqMKVKOVox7UKFzx+Awe+XBxtak0vj3+vWhTw3g1M7HN0WKDw/Jy5zt3v8+3x79X6WLrhD+/xc8emY67c/WTnwBw9ZMzcHc+W7WJp6Yt5bcTZvPCzBW8Omclf3vjs+i6kWZ8kaa/sY+fmhb0Y10d6cdape/nWf94P27NJsDWHWVxm9Wt3rQ9qMmqxepN2xPWzCUSeU+aNak47D5btSluMDpr2QYuefAjTvnr23FHMc2GbTvL4gYv4yd9xlPTljIhnAdy8sK1NW6nNKZGaNhNr3LN/4LP/LNVm6PL3Z1Rd7zJGXe/x7adZfzznYXR10bfEX/wo9gaKAhquCLH1k/CAAPgtLveSdjPb0MNA4D1at8y+vjZsNY2ItL8ONbitVvYUVpO05Lg83xh5oroa5+u2FipD3CkVjJy42fqoq8Yefsktu4o46onZ1Ta7u0vV+8TOm3xOobe+DJPfhQcm+8tiP8ZbNkefAfWbdmZ8LjZUVrOqDve5PWY2rp4NbRrNwffqw7hjaAVG7bRZ+yEagFp5L2ONMuft2IjfcZOqDZvKMDGbTu5+IGp0eeRG1Ox3D0aFEYCzC/WbGb2l9VvkCz+aiu/eHQ6o25/k53hdv75zkKO+MNr/OmVefQZO4Hpi9fF7ae8auP2aLAY20904DXPVarVjPjB/fFjmPkrN7E5bCb81vzkfxMWrt5cbdmOsvJqv2Px0kkFMysG7gRGAYOBM8xscGwad/+Duw9x9yHAlcAb7h77JRoRvj60vvItIiJSm8YVqMZpR3j54x9z9K1v8LfXP4uzRlBzUVVs0BjpU/jSrBXsftVEFoQXVb+bWBEgRvq6lZZ7tHnkc598yZ2vzefoW9/g0oemRZshPjN9WaUmfcVFxvqtO6v1L/zBvyZHa5xmLgsuQmNrkWIv9iKB+PL1W+kzdgKXPPgRe177PM98XP1C+vLHPubSh6bxjT+9yfbSMp6atpStO6rX9p11z/uceff7tQ58Ul7uPDZlCaUxF6DjnpvDLS8ENXZH3/oGh9/8WqVBgNyd0WHT0tWbdkRruiOWfLWFnzz4UaVAaFPYjHHlxm3c9tKnKc07u720jHc+W82gXz3P6Xe/x/XPzIxeeAfNdudw6UPTeGlWEIhFgr2N23byxZrN4QA3Fft7afaKStt/c17FxfvZ/3ifF2Z+Sd8rJ0aX3fbyp9z8fEUN5qwENbaxgSDALx/7OPr4mekVgeWHC79i+fqaB4pav2Un4yd9Vul9alJScfx036VFpUAvXrPWw29+jbFPfMzm7dWPjzlfbqwUfEeC2R2l5Sz5agun/vUd5ny5kQ/jBP3/jalhXRAG95HyvRzz3j46eTE3hd+zWcs28OunPmFLeIyNufNtrn+m8si4r8xewbjn5rBiwzZmL9/AFY9XvH+Dr32BzdtLKx2HkWAt0kQ8Ml3QI2E/UYC/vDqPJ8Lg+fLHPsbdOfa2oIn6ZTE1wpHvyaOTl0Q/m83bS9nz2uf586vzo+m+WLOZvldOZMWGyn2Aj/zD65wXp3ZxW/jd3FFWzs4q38U/hoNAjbnz7Wjz8Urvx5yVzAp/O2LLnah59poEA21FypKqBaviB6BbqvzeDL/l9bi/QRJ1IDDf3Re4+w7gIWBMDenPAB6sl5yJiIikoaT2JIUjMjBRvL6Jvw8HAqrqkpiaqojY2pffTpzNbydWr7WMXKTHDojyhxcqN6e85cWKmqNIEPL8zC/pHjbBhCAoO+626n0zI0ETVDTLjL24HfSr56OPX561gt2PaMUjHwZBdaQf2iUPfsTxe3Xl7kkLuOXFT7l69J68OieoLZq5bAP3vb2Qcc/N4XuH9uUXx+9By5g+kXPCgY1++vA0bjh57+jyqqOAPjplMVc8PoNf/e8TtsYEz395bT6XHTMg+rzfVRNp3ayEfl1ac9WoQZW2ERskLV+/lQc/WMQz05fxzPRlPHbhMCZ9uoo/hRf7R+zRmUmfruKIPTpzwG7tgSBYLioylq3bSpvmlQ/58//5Ia/Mqagh++DztXzw+VpKioyLRvSvVLsdGUDoDy/MZVi/jpz613eir/Vo1yL6ON4xE/HmvNWVAleAvzfAnKNX/28Gz368nBZNivnVUzM5Z9hu0aauEJRnS8znFW+UWYAnpi7lianxa+C/WFMRiNz79kIAtu4s47DfV/ShHp/kwFqRvoobtlZ89yKB+pWj9+Ssf7xfren7Mx8vq3Rsnh/WCB41qAtQfYCmvX79At8/rC/XnDCYKV+sjR6vD324iIP6dmBe2Ax73sqgFcSe3dpW+g5D5SArUhv6+erNjLjlda75xp6V3uNPw1rqO16p2EbVpq73vb2Q339zn4Tvy/SYG1jPz/wyYbpEbn4h/u9eqqoGl8lINFVRvNrtPa99noXjvpHyPhqJHsDimOdLgIPiJTSzlsBI4OKYxQ68aGYO/N3dx2croyIiIqnISKBqZiOBO4Bi4B53H1fldQtfHw1sAc5196nVNpQFG7ft5NU5K+nfpXW02eBfE9SeJuPJj5bQoVWzpNLG1ihB8hfly2Jqw578aFkNKQOR5pSJLlTfW7CGuSs2xg0oFq/dyu0vzwOoFnCPey64iL337c+59+3P+fTGUSz+agv9OreOppkwY3m0SSwQHQU0yPsSVobB89Y4U9L0v/q5Ss83bS9l+uJ1fHt85YFYYvsjDrvpVb65f8/o8/+7691KabeEF78zlqxj4ozlzFy2nlnLNnDCvt2r9YUEKgWpse5+83NemLki4bQoP314WqXn8Wqsclmkj+mvngoG57n/3S8qvb7kq618/5/pdVfbHtPyIFJjFznWIlJpKlpT+njNfPft2Y4L/z2FPbq2pmeHimbNkflp43n24+WMGdKDb/6tIs3itVsrHWfzV26q1n82YkbMjZqdZc5fX5/P8nXB9zlRM/zY+2afV2nm+tiUJZWaZFf13zjHdCpSHZk6kbpM9bQ9Qb/sl2fXva9vIxWvQ3aiJiUnAm9XafZ7qLsvM7MuwEtmNsfdq90dNbMLgAsAevfunW6eRUREamXpDv8f9o/5FDiW4E7uh8AZ7j4rJs1o4CcEgepBwB3uHveOb6yhQ4f65MnpXSz/8aVP+dMr82pPWMCaFhclHAV17KhB0YA0WZccPaDRv6dSu717tE16Dtd0vPaL4ZzwpzfZnKHmoQf17cD7n9fcD1nSt3/vdkwNR2lORiZqVM1sSqH1wzSzYcB17n58+PxKAHe/KU7aJ4FH3f2BBNu6Dtjk7rfUtM9MnJu/Hd4weviHw9LajoiI5Leazs2Z6KOaTP+YMcC/PPAe0M7MumVg37VatTH1O/2FpqapOlINUgEFqZKU+ghSIRgFO5NTDSlIrR+pBKlSow+BAWbW18yaAqcDT1dNZGa7AEcCT8Usa2VmbSKPgeOAT+ol1yIiIrXIRKAar39MjzqkAYLmRWY22cwmr1qV/hx6ieZAHLRrm7S3LXLKfnEP42r27tE2yzlp3Gqbn7WxOmKPzg2dBckydy8l6HP6AjAbeMTdZ5rZhWZ2YUzSU4AX3T22fXlX4C0zmw58AExw9+cRERHJAZkIVJPpH5N0Hxp3H+/uQ919aOfOmbnIateyCfed+/Xo858eswfPXXp49PnVo/fMyH5qc92Jg7nltH3rZV91dd6hfVJK36tDi9oT1eAP/5d4oJhk7durXbVmgbGD6CTjJ0f1p2Wc+TJbNEk8L+Q39+/Jbd8ektT2WzYt4ZmLD0spT4ncddYBGdlOvnng+7X2Foj69tBeWcnDmCHdE978qsmIgcFv2T/OGcr7Vx0NBDfLHvnhML7WY5e461Qd+KsuurRJrj99rvvOQeoTWRN3n+jue7h7P3f/bbjsLne/KybNP9399CrrLXD3fcO/vSLrioiI5IJMBKpLgNirwp5A1RGAkkmTVQf0aR99PGZId8wqYudRX9s1+vg/5x/EG78cnta+OrRqWm3Z9SftxbmH9o2bfvdOrdLaX110adOMg/p2AKBjTH77d2nN5zeNjj5v3qSIOTeM5IdH7F5tG/N+O4o3Lz+Kdi2bALBPz1341QnB9H2dYy6Qrxo9qNq6AG2alURHYK3qzctHVFt28pDucdM2Kap8H+ScYbsxZkh39u/djvvO/To/PGJ32jSruOhvHs7j2qdjMEjNFSMH8aPh/Zh5/fF89KtjK21r8jXH8PLPjog+Hz6w4ubJrd8Kbjq8cFnF69eftFfcPH7w+Vq+1nMXzjyoN2NHDeLmOgboQ3q1Y9juHeu0breY0aTzSafWwbG0W6dWvPyzI6KBXiLdd2nO72t4f1s3K2H6tcexa9vK78d1J1ZMPdm1bfwA7/ZvD+HT346K+9rM64+PPv7l8QO579yvc0i/4LMaO2pPPr9pNEfv2ZWubZuz4Hejee7SwzmwbwcevXAYx+zZFYC5N46MbuO9KyuX88g9OnPhkf2Y9MuK78Zt3675xtevThhMv87xf19qumn282P3qHG7idwwJv7x379L60qjmafqt6d8rc7rioiISH7KxKi/0f4xwFKC/jFnVknzNHCxmT1EMJjSenevPolnFkSqbds2b8Ld3x3KV1t20KdKYBgbVB02oFN0vs/aBvu4/3sHcs69H0Sfd2rdlNWbdnDykB5ce+JgHvxgEVc+MQOAoWGgHO+iMZVmiwvHfYM+YydUWjb+7AO44N9Tkt4GBE0CIyOxDurWhrfnrwGgSXERZsYdpw8J5ncth+ZNihk7ahA/O24PpnzxFWfe/X40LcCky0fw1LRlHL9XV7q0ac75h/Vl286y6BQ5kUAD4O7vDuWQfh1Zv3UnLZsW065lRZB88pDuXDSiPwO6Bs2yn7roUF6ZvYKfHTcQgE9XbOR/06rf34jcc/jx8H68//larh8T1KY+8eNDARgxqAtnHbwbVzz+MVeMHMTunVtRWuY0KSliy45SurSpuIBuHxO0z7lhJM2bFNO/SxsO7NuBDz5fGw32Ymu7BsY0Iz9taE+KrGI03e8O243Zyzdw1KAgEPldzAX3/r3bccwfKw+u+ecz9uMnNUxv89/vH5RwOM/ajBjUJTry8XUnDua6KvOMZtJ3DupdaUTaNs1K2JjCXJutm5VEpy+5+7sH8NiUJXTfpXmlG0xV7d6pFQtWb67UX7W4yCrNEdqxVVOmhDcjrv7GntH3uvsuzTn30L7Rm0mTPl3Fd2O+2xE17b9VsxJe+8VwOrVuSpvmwc2bEYO6sLOsPPpdiSiKubnSvEkxd521P9tKy2lWUlGD37JpMTd/cx8uf/xjzjyod6VjJ+KIATW3OtmlRRNO3b9ntamxAA7s06HasuMGd+XMg3rTu0NLbn3pU5qVFCX8ffrB4X25+83K0xadPawPx++9Kwf+9pVKyy88sh8n7NOt0rRZIiIiIjVJu0Y1yf4xE4EFwHzgbuDH6e43FZFLwmMHd+VbVZoEnrRv9+jF4TF7BrV7zZsU89RFh3LPOV8nnkgT0dhL1jcvH8EDPzg4uh+g0jQqkeaC+/Vuz6RfjmDyNcfw+I+C0Q5rGuwo1k+O6h93+XF77Rp3+YiY2r8ubZpx7iF9+Nmxe/DiT4/gt6fszUUjgu19snRD9KI1ks9ITWckb2ZGs5JiDunXibu/O5RfHFdR49K2eRPOPni3SgFf8ybFRK7Fm8c0n+3QqgmtmpXQvV2LSkEqwO2n7xcNUiFo0hsJUqGiRvDWBDVBl48cxOM/OiTua706tOSBHxzMvr3a0aZ5E9q3akrrZiWV8hxx8//twx2nD6mU718eH+TjsP7he5ogWmxSXERxUcXX6ltDe/HohYfwo+H9qqXt36V6P+kjByYOPC4e0Z9WzUooKUocLAFMuOQw3hl7FI9UGU3zuhMrarvOOaQPb14+glm/OT4j/bXv/96BlWpsD+xbEQSddXBvnrzo0Erp+9bSiiD25tF+vdvz21O+VmOQCHBa+N2OBKpvjz2KqdccW2lfXWJqUU/ct3u0FvSyYyrXIBaH7/FBMeVoUlzz/iEoVyRIrViv9p/ZkuIiWoe1/pFmrmbGSUO6c9bBvflFzPcgVpEZ48+O3xQ80hKgLMFAU7vGqeH87rA+DB/YhaYlQZ7LaxgV/idHD+DU/av30Y73nSqy2t+HSEuHiJvD+WO/l6AlioiIiBS2jMyj6u4TCYLR2GWxfWMcuCgT+8qk2Caun1x/PM1KKi6U9u3VLuF6vTu0ZM6XG2kR06exVzhPY2xfyaYlRXRp04yVG7dXukjrHTY5jVwD7kiyRvXnCS5WAQ7p15F3PltTadlvxuzNrrs0Z+O20rjNkft3ac0lR/XngD4deDCs/YrkM3KxffbBu1Vb79jBXaPBeE1+cPju/H3Sgkrva5+O1QOUJ358CK8nmM80VpvmTaLvb5OSIpav28pNdRi1uDZVb2YAfL1PB+bcMDI6Z21Zggv4kiIj8lF/a2hP9k7Q/zCRtlWCnFg/C5tj1hao7tU92OfGbRU1mB1aNY0GHxAEQZFjdmif9sz5cmNK+azqyD068+6VR0dr+4tigsobT65eEzi4e9tqc4bGGjtqED9MsZVAh1bBezdq7+DGTY92Qf/p4QM7R/dVtUlvq2Ylcac9iQS7TYqLuHzkQNZu2sEP4jR/z4YbT96b68Im5M2bFMd9/yKKzCrdqNqtY0u+WBPM/Ru5EVJRFmNnWcVxG3s8RESC18hrkSC3Y6umrNm8o1LaYjPGjhoUnZ/53EP6JMynWUXwD/CbMXtxbdjqIKJFk2K2hXPv/vaUvfnW13tx5MDOlbomiIiISOORiT6qecvMorU0rZuVxL3jH68S56ELDuaGk/dm6G7tOWKPzglrNKCi4i3eRWGk+egRe3TmgiN2p0Orpnx+0+ha5wuM19fwgR8cHLdfZ5PiorhBasTPjhvIkXt0jgZgsbVGC343OuVBiWKNHTWIheO+QUn4vh4+oBMdW1fv+7d/7/aVak6TcdK+3RkS3kywuGN1ZV7zJsXRY2S/3u3ipjGzaI1qqlOmvPLzIwGYeMnhcV+PNBctriFQnXHdcdHHA5OsKf3VCYP523f2TzabSaktmMbhzBoGyOnXuTXvX3U0//regXFfj9TYXnJU/+j3qE3zJky55phq/YR/cdxABoY19VX7pSayYetOANq2KOHHw/tzzQmD6Rpn3Z8eU7e+nDUxs6RqYQGsSrJ/nncg/z7/QP5y5n7RZeXhcRhvhOqvh10SWoU33dq2CN7LZsXB83IPauhf+OkR1dYtMqNLm+actG/Qd3zfXolvyhRV+SEdEudGYMumFfdNR+8dzF7WtW3z6O+HiIiINC4ZqVHNZTW0XEtKv86tmb9yExA0Db5oRH/atWwarWlMdCFdsf8gAyVxmg02b1LMm5ePoHObZjRvUsxVSY4+HHvRFztQUa8OLbnrrP258D9Tg3S1BQsxIk18YwPqVNaPJ3IToDj8n6gJYl1ldmvJad6kmGcuPoy+CQaogYogLZXy3nfu1+nXuTWQeCCfiNgmsHeddQB9OrVk5O1vAlRrdhrxmwSD3AA0Kylm1NeSn9b4R8P78bNj98Ad9rjmubhp4gXTZx7UO9pH9pT9ejC4e9vo86cuOpQxd74NwIs/PYL+XSLvRfzA8l/fO5AtO8ro0Kops5Zv4OXZKykpsrg3Qlo1K+GRHw7j4gencukxA5IqY6S58Mi9478vT198KJ+v3szSdVuT2l62VA0Ai804vEq/1QFdg/dy0K7Vp0i6+7tDmbZ4HU1Linh7/upos93Y34FIDf0/z/s6Xds2Z9QdwbEWaeEe6QoRqQ2FoM/vsvWJ57Deu3v1oDa26W971aKKiIg0egUfqELNA6DUZmDXNsxfuYnbvr0vp+zXs/YVqogEyolq/SLNLxN5+uJDOekvb1daFinOpF+OiDYjjhi5dzeKLKgJSSXObBXWZsTWamRK5II204FqVP1UqEZ9rWfNzXkjAX4qNaojYkY/7tCqKRce2Y+Re+9K62bF1QZcijhmz66MDJu5njyke9yBpiJO2Ceo9XrvyqMpLY/f1Pz7h/Vl/93a8+P/Tq0xr+XulWr84gWl8ZZdPXpP3J2xo/ZklxZNooOWQeWm9nt0rb0muHmT4mgf4shxFe9mUMQuLZvw7/OTn95m7x67MO3aY6v1o47Yp2c79unZji07Slm+bhtXjIo/snW2VX2bi+O8Byft251+nVvTsXVTfvNsMIDWyz8Lau/btWzK8IHBsXdIv07RdeK1AImkMwt+1yJBcuRz2LKj4vN85ieHccCNL8fkM0g7YmBnRn2tW9ybYNn47REREZH8pSuDWlx74mDK3Tl6z9r7ZMZzxB6defKjpZX6syYjtvnvL47bg+7tKuYrjVz01TTQSWy6ZPzu1K+x/27tok0BM2mXFkEtX8/2NQflqaq4CZBbWoYX7q1S/MwjLOz7V5Ppvz6u0ryvt35rCDedWn1Klt+M2atSrWS8AXQirjlhcKXnVUfMjYg97KZcc0zcoDRSs7tnt4pavFbNSirlsS7zkcYTuSGQyvGejERBaqyWTUvSah6frqplbhtn7lUzY+8eu/BlWMPZtW2zaI11IpHPdLeO1b+z154wmOufmRVtKRE5DrfuqOgTHa9mG+C+8xK3QEn1N1JEREQKmwLVWnRt25y/nZW4D2ptxn3za1x2zIDoiJ51cfFRlZsrRuKCRIFqZGkq1+0dWjXlgiOqj0ybCXt134W7zjqAI/boVHviAnDUoC5cMXIQ3zk4cR/MiImXHM4nS9fXmObCI/tx1xufVVoWCf4jioss7oX+d4f1qT3DCbx/1dEMjakViyiPne6lSkBy55n78+/3FvL1Pu35w//tw+gamhSn27Q8IvI1KClqfH0Zq37H0/mdqeqB7x9E/67VA9rzDu3LeTEj8bYIb8xsjakhh6BZ8Q/+NRmAjq2TCfoVqIqIiEiFxndlV8+alRSzW5yRbtMRqUWprWFppmuY0jFy710Lumlf7HQsRUXGj4b3q3EE34jB3dvyra9XH2U4VmRQqvrWKUGtWE0tmr+xTzceumAYZsZpQ3vRqpbA6efH7sGTP44/pVCyIrW+jTBOrfYdT6abQ7L99g/p3ynuVDNVHbBb0Arja1VGuG4RM71TbLPiiFm/Ob7S8+F71DwnrIiIiDQuhRs5hLxBhtzJrsi1qCeqUQ0X51Kgmg2RzzYXivnQDw4uwCMtvkx+p35ydHKDG9WkLEtNf/NBKmXO1ttzSP9OvH/V0dUGvopUmPdO0A8/9sbVzOuPZ82mHVz3zKyc+D6LiIhIwyv4QBVyrw9juob168hnqzYnHOE1IkMtK3NXDkWGmWrGmutO2a8HPzkq/eAykyJz2tY0bU+hOXxAJ96ctzpnftvijc5sVfqwxvPm5SNo3qSYVs1KWB9OCdQYbziIiIhIdY0iUC00vz5xL849pG/CqTsi0hntOJ/U1zyqjcVvT9mbdVt2xn3ttm8Pqd/MJKEx1qj+/ewDWL5+W0o3SCIpk52jNV2Rj6OmQZJiRz2PjNrciO43iIiISA0UqOahJsVFtY7aCYV/wVeXQaOkdt85aLeGzkJKyhthjWrLpiXReXcjOrepef7dzm2acclR/Tl5vx7ZzFrUznBu5mQHSWoSdjJuLDfYREREpGYFH6gmO3BIISr0GqbIlD2H9m8cowk3hMHd2jJr+YaGzkaNmpcEgVBJIwpUq5p8zTHR+UwTMTN+dtzAespRxbyqLZokd5pRjaqIiIjEKvhAFRpvjVuhB6p9O7XinbFHsWstTaCl7h764cGsWL+NY2+bRMdWtU8xko5OrZvRq0OL2hNWcccZQ3j4w8Xs1b1t7YkLVKIRmhvS1jBQTbZGNVIj3j6J+WtFRESk8DWKQLWxKvA4FaioVZXsaNu8CW2bN+HBHxzM7p0zO81SVZOvOaZO63XbpQWXHbNHhnMj6SoN+w63b1n7NE0QNGf+9YmDOWpQl2xmS0RERPKEAtUC1hgCVakfw/p1bOgsSJ45ad/uzF+5iYtG9Et6nfMO7ZvFHImIiEg+KfhAtRF3US34pr8ikrualhQxdtSghs6GiIiI5Kn6maegwTXOgE2BqohI4TOzkWY218zmm9nYOK8PN7P1ZjYt/Ls22XVFREQaSsHXqDZmGj1TRKSwmVkxcCdwLLAE+NDMnnb3WVWSvunuJ9RxXRERkXqXVo2qmXUws5fMbF74v32cNL3M7DUzm21mM83s0nT2KbXbs1sw+qnmIxQRKXgHAvPdfYG77wAeAsbUw7oiIiJZlW7T37HAK+4+AHglfF5VKfBzd98TOBi4yMwGp7lfqcED3z+Ix380rKGzISIi2dcDWBzzfEm4rKphZjbdzJ4zs71SXBczu8DMJpvZ5FWrVmUi3yIiIjVKN1AdA9wfPr4fOLlqAndf7u5Tw8cbgdkkOBFmgzfC0ZTat2rKAbt1aOhsiIhI9sVrOlP1zDcV2M3d9wX+DPwvhXWDhe7j3X2ouw/t3LlzXfMqIiKStHQD1a7uvhyCgBSocQI8M+sD7Ae8X0OajN+1VQtYEREpUEuAXjHPewLLYhO4+wZ33xQ+ngg0MbNOyawrIiLSUGodTMnMXgZ2jfPS1ansyMxaA48Dl7n7hkTp3H08MB5g6NChjbA+VEREJGkfAgPMrC+wFDgdODM2gZntCqxwdzezAwluUq8B1tW2roiISEOpNVB192MSvWZmK8ysm7svN7NuwMoE6ZoQBKn/dfcn6pxbERERiXL3UjO7GHgBKAbudfeZZnZh+PpdwP8BPzKzUmArcLq7OxB33QYpiIiISBXpTk/zNHAOMC78/1TVBBYMPfsPYLa7/zHN/dWBKmVFRKRwhc15J1ZZdlfM478Af0l2XRERkVyQbh/VccCxZjaPYB62cQBm1t3MIie+Q4GzgaNiJhsfneZ+U6IuqiIiIiIiIvkjrRpVd18DHB1n+TJgdPj4LRQrioiIiIiISJLSrVEVERERERERyaiCD1Qb4zyqIiIiIiIi+azgA1URERERERHJL40iUDX1kBUREREREckbjSJQFRERERERkfyhQFVERERERERySsEHqhpMSUREREREJL8UfKAKYJrGVUREREREJG80ikBVRERERERE8ocCVREREREREckpBR+oOuqkKiIiIiIikk8KPlAFzaMqIiIiIiKSTxpFoCoiIiIiIiL5Q4GqiIiIiIiI5BQFqiIiIiIiIpJTCj5QdY2lJCIiIiIiklcKPlAF0FhKIiJSqMxspJnNNbP5ZjY2zuvfMbOPw793zGzfmNcWmtkMM5tmZpPrN+ciIiKJpRWomlkHM3vJzOaF/9vXkLbYzD4ys2fT2aeIiIgEzKwYuBMYBQwGzjCzwVWSfQ4c6e77ADcA46u8PsLdh7j70KxnWEREJEnp1qiOBV5x9wHAK+HzRC4FZqe5PxEREalwIDDf3Re4+w7gIWBMbAJ3f8fdvwqfvgf0rOc8ioiIpCzdQHUMcH/4+H7g5HiJzKwn8A3gnjT3lzJ1URURkQLWA1gc83xJuCyR84HnYp478KKZTTGzCxKtZGYXmNlkM5u8atWqtDIsIiKSjJI01+/q7ssB3H25mXVJkO524HKgTZr7qxMz9VIVEZGCFO8EF/cerZmNIAhUD4tZfKi7LwvP3y+Z2Rx3n1Rtg+7jCZsMDx06VPeARUQk62oNVM3sZWDXOC9dncwOzOwEYKW7TzGz4UmkvwC4AKB3797J7EJERKSxWgL0inneE1hWNZGZ7UPQqmmUu6+JLHf3ZeH/lWb2JEFT4mqBqoiISH2rNVB192MSvWZmK8ysW1ib2g1YGSfZocBJZjYaaA60NbP/uPtZCfanu7YiIiLJ+RAYYGZ9gaXA6cCZsQnMrDfwBHC2u38as7wVUOTuG8PHxwG/qbeci4iI1CDdPqpPA+eEj88BnqqawN2vdPee7t6H4AT6aqIgNRs0j6qIiBQqdy8FLgZeIBiw8BF3n2lmF5rZhWGya4GOwF+rTEPTFXjLzKYDHwAT3P35ei6CiIhIXOn2UR0HPGJm5wOLgNMAzKw7cI+7j05z+yIiIlIDd58ITKyy7K6Yx98Hvh9nvQXAvlWXi4iI5IK0AtWwn8vRcZYvA6oFqe7+OvB6OvsUERERERGRwpZu018RERERERGRjFKgKiIiIiIiIjml4ANVjz+dnIiIiIiIiOSogg9UASzedOgiIiIiIiKSkxpFoCoiIiIiIiL5Q4GqiIiIiIiI5JTCD1TVRVVERERERCSvFH6givqoioiIiIiI5JNGEaiKiIiIiIhI/lCgKiIiIiIiIjml4ANVdVEVERERERHJLwUfqAIY6qQqIiIiIiKSLxpFoCoiIiIiIiL5Q4GqiIiIiIiI5JSShs6AiIiISMTGbTs1vkQ9cb3RcZWVO2/NX82c5RsYtGtbDuvfieIidSPLF65fkKwzjF1aNsn6fgo+UHX9CouIiOSN2cs3Ulaeu+fu8nJn2uJ1LFyzmT4dWzGkVzuKGjiIycU85avycud3z81m/spN7Cgtp2lJEf27tOaqUXvqPY2hY65xKyk2vt6nQ/b3k/U95ADT90ZERETSlItBTK7mKV+DmGmL1zF/5Sa2l5YDsL20nPkrNzFt8Tr23619xvaTa+9RKvmpyzGXa+WV2uXCZ9YoAlUREZFCZWYjgTuAYuAedx9X5XULXx8NbAHOdfepyaybC3LhYimivoKYXMtTtoOYXLJwzWZ2hO9lxI7Schau2ZzR9zOXAr1U85PqMZeLx0S2f1dy6XerLnLlM0srUDWzDsDDQB9gIfAtd/8qTrp2wD3A3gRTm37P3d9NZ98iIiKNnZkVA3cCxwJLgA/N7Gl3nxWTbBQwIPw7CPgbcFCS6zao+rigTyV9XYKYulywZjtPqch2EFPXPGXrM+7TsRVNS4qi+QdoWlJEn46tMpJ3qJ9AL5Uyp5qfVI+5XLvBk+0grK7bz6XgOVc+M0unD6eZ3QysdfdxZjYWaO/uV8RJdz/wprvfY2ZNgZbuvq627Q8dOtQnT55c5/wBXPrQR0xbvI43fjkire2IiEh+M7Mp7j60ofORSWY2DLjO3Y8Pn18J4O43xaT5O/C6uz8YPp8LDCe4yVzjuvF02G1PP/aqe9PK96zlGwAY3K1ttdc2biuNDoaycVspS9dtrTTojxn0aNeCNs2r32t3dxat3crWnWW4B2lbNCmmd4cWWJx+QKmmz3Z+6iNPqUp1+6s2bmf1ph3Vlndu3ZRObZqlnZ9sf8aR9Ft2lEXLWttnlqpU36NsH3fZzk+2jwkIyrxpexnbdpbRvEkxrZsVJ/y8cu07E8l/XX4rki1zpo8JwzLyXgE8cuEhCc/N6U5PMwa4P3x8P3By1QRm1hY4AvgHgLvvSCZIzaT8qWgXERFJSQ9gcczzJeGyZNIksy4AZnaBmU02s8k7d+5MO9ODu7WNG6RWtS28qIrlDtt3lsVNv2l7WfRCLJJ2684yNm3PTPrWzYpp0aQ4OvZF5GKvdbPijGy/PvIU8cWaLXyxZkuNaSD1z6B5TF4izKBZk8T5cXcWrNrM/JWbghsVNVSiZPszNjN6d2hBz/Yt6Ny6KT3atUgqSE32/YTU36Nsfw9SzU+qx1y2j4lIELbkq62s3rSDpeu2smjt1oTrpPp+ppqfumw/1c8s1TJn+5jIlnRD4a7uvhzA3ZebWZc4aXYHVgH3mdm+wBTgUnffHG+DZnYBcAFA796908yeiIhIQYt39Vz1SiVRmmTWDRa6jwfGQ9Da6eEfDksljyn54PO10VF/p37xFX96dV6lZpjNSoo495C+cZufPTF1CY9NWVIl8zBs946cun/PtNNDas3n6rL9bOcp4jfPzgTg2hP2qjFdqp9Bqs0eI+l3lJXjHtTk7NIicfr6+IxTVV7uXPHEx2zbWcYJX+ueVFPkVN6jbH8Pst20ONvHROT9iRbVgymGTtynR9z3p67HdKr5SXb7kPpnlmqZM31MZHLU30cuTPxarYGqmb0M7BrnpauT3H8JsD/wE3d/38zuAMYCv4qXuOrJMMl9iIiINEZLgF4xz3sCy5JM0zSJdRvUkF7t6N+ldbWLpSG92sVNn2r/wrr0RywqMvbfrX1S/bTqsv1s5wmCi9CN20rZtrOMqV98VWOQkepnUFRkXDVqz5T7wkVqemrrC1cfn3EqIhf0kaaef3p1Xq1BXqrvUba/B6nmJ7JOssdcto+JVPvMpvp+ppqfVLcPqX9mqZa5Po6JbKg1UHX3YxK9ZmYrzKxbWJvaDVgZJ9kSYIm7vx8+f4wgUK0XmkZVREQK2IfAADPrCywFTgfOrJLmaeBiM3uIYDCl9eF5e1US6zaobF/Q1+WCMhV12X6285RqYJXtICbbQUa2389Ug5iIbAZ6dSlzqjc7UpXNYyLbQViq+anLdybbNyNy8ZhIRrpNf58GzgHGhf+fqprA3b80s8VmNtDd5wJHA/U6omCmOr+LiIjkEncvNbOLgRcIppi5191nmtmF4et3ARMJpqaZTzA9zXk1rdsAxahRNi/os11rUNcgL5t5qktglc0L1mwHGdl+P+tjOhvI7vcg1+RaEFYfrRyyfTMiX4+JdAPVccAjZnY+sAg4DcDMuhPMxzY6TPcT4L/hiL8LCE+SIiIikh53n0gQjMYuuyvmsQMXJbtuQ+vTqWWCnrLJG9C1dVbTpyqy/VSK1T9LeXpt7sq4gdWGbTvZvXPmpmBJ1m4dW/La3JXMXLYhOnrpXt3bcsr+PSiu4SI61fcnkj7TLe0O6tuRZz9eztaYgXKaNynmoL4d6dup/t/PWP26ZPe4zpbeHeIfE2P2657wmPj72Qfw7mdrmLdyIwO6tGFYv441Hj+p6NWhRcr5qavduyR/zNSlzKlsvyZF9VQJmFag6u5rCGpIqy5fRnD3NvJ8GlBQUwKIiIhI5nVp07yhs1DQDurbgf+890V0+hWAFk2LObBvB7q2bZj3/uEfDuP1uSuZtWwDg7u3ZfjALhkPALLllP178MRHS5i2eB1bd5TRomkxQ3q1qzXQlprV5Zg4bWjLnMpPfchmmXNBZibAEREREZGcN3xgF4b0alctsBo+MN7EDfWjuMg4es+uHL1n1wbLQ10VFxn/Pv+gnAxi8lmuHRO5lp/GouADVY2lJCIiIhJQYJV5CmJEsqPgA1WIP1GciIiISGOkwEpE8kFRQ2dAREREREREJJYCVREREREREckpBR+oeqbHIRcREREREZGsKvhAFVAnVRERERERkTzSOAJVERERERERyRsKVEVERERERCSnFHygqh6qIiIiIiIi+aXgA1VQF1UREREREZF80igCVREREREREckfClRFREREREQkpyhQFRERERERkZxS+IGqRlMSERERERHJK4UfqAJmGk5JREREREQkXzSKQFVERERERETyR1qBqpl1MLOXzGxe+L99gnQ/NbOZZvaJmT1oZs3T2a+IiEhjl8w52Mx6mdlrZjY7PA9fGvPadWa21MymhX+j67cEIiIiiaVbozoWeMXdBwCvhM8rMbMewCXAUHffGygGTk9zv0lzdVIVEZHCVOs5GCgFfu7uewIHAxeZ2eCY129z9yHh38TsZ1lERCQ56QaqY4D7w8f3AycnSFcCtDCzEqAlsCzN/aZEPVRFRKQA1XoOdvfl7j41fLwRmA30qK8MioiI1FW6gWpXd18OwckQ6FI1gbsvBW4BFgHLgfXu/mKiDZrZBWY22cwmr1q1Ks3siYiIFKxaz8GxzKwPsB/wfszii83sYzO7N1H3nXBdnZtFRKRe1RqomtnLYd/Sqn9jktlBeOIbA/QFugOtzOysROndfby7D3X3oZ07d062HCIiIgUn3XNwzHZaA48Dl7n7hnDx34B+wBCCG8m3Jlpf52YREalvJbUlcPdjEr1mZivMrJu7LzezbsDKOMmOAT5391XhOk8AhwD/qWOeU+LqoioiInkqA+dgzKwJQZD6X3d/ImbbK2LS3A08m7mci4iIpCfdpr9PA+eEj88BnoqTZhFwsJm1tGBC06MJ+siIiIhI3dV6Dg7Pu/8AZrv7H6u81i3m6SnAJ1nKp4iISMrSDVTHAcea2Tzg2PA5ZtbdzCYCuPv7wGPAVGBGuM/xae43JabRlEREpPDUeg4GDgXOBo6KMw3NzWY2w8w+BkYAP63n/IuIiCRUa9Pfmrj7GoIa0qrLlwGjY57/Gvh1OvsSERGRCsmcg939LRIMfu/uZ2c1gyIiImlIt0ZVREREREREJKMKPlDVYEoiIiIiIiL5peADVQCL3+pJREREREREclCjCFRFREREREQkfyhQFRERERERkZxS8IGqo06qIiIiIiIi+aTgA1XQPKoiIiIiIiL5pFEEqiIiIiIiIpI/FKiKiIiIiIhITlGgKiIiIiIiIjml4ANV11hKIiIiIiIieaXgA1URERERERHJLwpURUREREREJKcoUBUREREREZGcUvCBqrqoioiIiIiI5JeCD1QBzKyhsyAiIiIiIiJJahSBqoiIiIiIiOSPtAJVMzvNzGaaWbmZDa0h3Ugzm2tm881sbDr7FBERERERkcKWbo3qJ8CpwKRECcysGLgTGAUMBs4ws8Fp7jdpmkdVREQKkZl1MLOXzGxe+L99gnQLzWyGmU0zs8mpri8iItIQ0gpU3X22u8+tJdmBwHx3X+DuO4CHgDHp7DdV6qEqIiIFaCzwirsPAF4Jnycywt2HuHts66dU1hcREalX9dFHtQewOOb5knCZiIiI1N0Y4P7w8f3AyfW8voiISNbUGqia2ctm9kmcv2RrReNVaCZskGtmF5jZZDObvGrVqiR3ISIi0uh0dfflAOH/LgnSOfCimU0xswvqsL7OzSIiUu9Kakvg7sekuY8lQK+Y5z2BZTXsbzwwHmDo0KHqYSoiIo2Wmb0M7BrnpatT2Myh7r7MzLoAL5nZHHdPOLZEPDo3i4hIfas1UM2AD4EBZtYXWAqcDpxZD/sN6XwqIiL5qaabxWa2wsy6uftyM+sGrEywjWXh/5Vm9iTB2BGTgKTWFxERaQjpTk9zipktAYYBE8zshXB5dzObCODupcDFwAvAbOARd5+ZXrZTzWd97k1ERKRePA2cEz4+B3iqagIza2VmbSKPgeMIRuxPan0REZGGklaNqrs/CTwZZ/kyYHTM84nAxHT2JSIiIpWMAx4xs/OBRcBpENwsBu5x99FAV+BJC+7YlgAPuPvzNa0vIiKSC+qj6a+IiIhkmLuvAY6Oszx6s9jdFwD7prK+iIhILqiP6WkalKuLqoiIiIiISF4p+EAV1EdVREREREQknzSKQFVERERERETyhwJVERERERERySkFH6iqi6qIiIiIiEh+KfhAFcBQJ1UREREREZF80SgCVREREREREckfClRFREREREQkpyhQFRERERERkZxS8IGqu4ZTEhERERERyScFH6gCmMZSEhERERERyRuNIlAVERERERGR/KFAVURERERERHJKwQeq6qEqIiIiIiKSXwo+UAVQF1UREREREZH80SgCVREREREREckfClRFREREREQkp6QVqJrZaWY208zKzWxogjS9zOw1M5sdpr00nX2mStOoioiIiIiI5Jd0a1Q/AU4FJtWQphT4ubvvCRwMXGRmg9Pcb2o0kaqIiBQYM+tgZi+Z2bzwf/s4aQaa2bSYvw1mdln42nVmtjTmtdH1XggREZEE0gpU3X22u8+tJc1yd58aPt4IzAZ6pLNfERERYSzwirsPAF4Jn1fi7nPdfYi7DwEOALYAT8YkuS3yurtPrI9Mi4iIJKNe+6iaWR9gP+D9GtJcYGaTzWzyqlWr6i1vIiIieWYMcH/4+H7g5FrSHw185u5fZDNTIiIimVBroGpmL5vZJ3H+xqSyIzNrDTwOXObuGxKlc/fx7j7U3Yd27tw5lV2IiIg0Jl3dfTkErZeALrWkPx14sMqyi83sYzO7N17TYRERkYZSUlsCdz8m3Z2YWROCIPW/7v5EuttLhcZSEhGRfGVmLwO7xnnp6hS30xQ4CbgyZvHfgBsITpU3ALcC30uw/gXABQC9e/dOZdciIiJ1Umugmi4zM+AfwGx3/2O29xc3Dw2xUxERkTTVdLPYzFaYWTd3X25m3YCVNWxqFDDV3VfEbDv62MzuBp6tIR/jgfEAQ4cO1T1gERHJunSnpznFzJYAw4AJZvZCuLy7mUUGZTgUOBs4SiMLioiIZMzTwDnh43OAp2pIewZVmv2GwW3EKQQj+YuIiOSEtGpU3f1JKo8eGFm+DBgdPn4LVWqKiIhk2jjgETM7H1gEnAbBzWLgHncfHT5vCRwL/LDK+jeb2RCCpr8L47wuIiLSYLLe9LehuauFkoiIFB53X0Mwkm/V5dGbxeHzLUDHOOnOzmoGRURE0lCv09M0FFN9roiIiIiISN5oFIGqiIiIiIiI5A8FqiIiIiIiIpJTFKiKiIiIiIhITmkUgaq6qIqIiIiIiOSPRhGoioiIiIiISP5QoCoiIiIiIiI5RYGqiIiIiIiI5JSCD1TdGzoHIiIiIiIikoqCD1QBzDSckoiIiIiISL5oFIGqiIiIiIiI5A8FqiIiIiIiIpJTSho6A9l2YN8O7Cgtb+hsiIiIiIiISJIKPlC95OgBDZ0FERERERERSYGa/oqIiIiIiEhOUaAqIiIiIiIiOSWtQNXMTjOzmWZWbmZDa0lbbGYfmdmz6exTREREREREClu6NaqfAKcCk5JIeykwO839iYiIiIiISIFLK1B199nuPre2dGbWE/gGcE86+xMREREREZHCV199VG8HLgdqnSfGzC4ws8lmNnnVqlVZz5iIiEg+Srb7jZmNNLO5ZjbfzMbGLO9gZi+Z2bzwf/v6ybmIiEjtag1UzexlM/skzt+YZHZgZicAK919SjLp3X28uw9196GdO3dOZhUREZHGqNbuN2ZWDNwJjAIGA2eY2eDw5bHAK+4+AHglfC4iIpITap1H1d2PSXMfhwInmdlooDnQ1sz+4+5npbldERGRRsvdZwOYWU3JDgTmu/uCMO1DwBhgVvh/eJjufuB14Irs5FZERCQ1tQaq6XL3K4ErAcxsOPCLZIPUKVOmrDazLzKQjU7A6gxsJ9c1hnI2hjKCylloGkM586GMuzV0BhpAD2BxzPMlwEHh467uvhzA3ZebWZdEGzGzC4ALwqebzKzW8SmSkA/HTCY1tvJC4ytzYysvNL4yN7byQvbLnPDcnFagamanAH8GOgMTzGyaux9vZt2Be9x9dDrbd/eMtP01s8nuXuP0OYWgMZSzMZQRVM5C0xjK2RjK2BDM7GVg1zgvXe3uTyWziTjLPNV8uPt4YHyq69WksR0zja280PjK3NjKC42vzI2tvNCwZU4rUHX3J4En4yxfBlQLUt39dYKmRSIiIlKLDHS/WQL0inneE1gWPl5hZt3C2tRuwMo09yUiIpIx9TXqr4iIiNS/D4EBZtbXzJoCpwNPh689DZwTPj4HSKaGVkREpF40lkA1o82VclhjKGdjKCOonIWmMZSzMZQxp5jZKWa2BBhG0P3mhXB5dzObCODupcDFwAvAbOARd58ZbmIccKyZzQOODZ/Xp8Z2zDS28kLjK3NjKy80vjI3tvJCA5bZ3FPuqiIiIiIiIiKSNY2lRlVERERERETyhAJVERERERERySl5GaiaWS8ze83MZpvZTDO7NFzewcxeMrN54f/24fKOYfpNZvaXmO20NLMJZjYn3E5998+pUabKGb72vJlND7dzl5kVN0SZqspkGWO2+bSZfVKf5ahNhj/L181srplNC/8Szn1Y3zJczqZmNt7MPg2/o99siDLFk8HfoDYxn+M0M1ttZrc3ULEqyfBneYaZzTCzj8Pfok4NUSbJDWY2MvwNm29mYxs6P/XBzBaG34FpZja5ofOTDWZ2r5mtjD3/Jvq9KAQJynudmS2N+U1Pa5rGXJLqOaEQ1FDmgvyczay5mX1gFfHC9eHyBvuM87KPqgXD6Hdz96lm1gaYApwMnAusdfdx4cmvvbtfYWatgP2AvYG93f3icDstgYPc/TULRkN8Bfiduz9X/6WqLlPlDLfV1t03mJkBjwGPuvtD9VykajJZxnB7pwL/B+zj7nvXY1FqlOHP8nXgF+6ecxc7GS7n9UCxu19jZkVAB3fPiUm2M33cxmx3CvBTd59UH+WoSQZ/Z0sIpkMZ7O6rzexmYIu7X1fvhZIGZ8FN0k8JBm9aQjAq8RnuPqtBM5ZlZrYQGJorv2HZYGZHAJuAf0XOv+H3vdrvRUPmM1MSlPc6YJO739KQecuGVM8JDZfTzKmhzN+iAD/nMEZo5e6bzKwJ8BZwKXAqDfQZ52WNqrsvd/ep4eONBCMZ9gDGAPeHye4nOJhw983u/hawrcp2trj7a+HjHcBUgjnmckKmyhm+tiF8WAI0pQ4TvmdDJstoZq2BnwE3Zj/nqclkOXNZhsv5PeCmMF15Ll3gZePzNLMBQBfgzezlPHkZLKOFf63Ck2BbKubxlMbnQGC+uy8Iz7sPERxTkufCG2xrqyyO+3tRCBKUt2Clek4oBDWUuSB5YFP4tEn45zTgZ5yXgWosM+tDcBf/faCruy+H4OAiuOhLdjvtgBMJalVzTibKacHUBSuBjQS1qjklA2W8AbgV2JKtPGZCho7Z+8LmJr8KL/5zTjrlDL+PADeY2VQze9TMumYxu3WWqd8g4AzgYc/BZi7plNHddwI/AmYQ1qwC/8hmfiWn9QAWxzxfQgFf+MVw4EUzm2JmFzR0ZupROr+J+epiC7o53FtIzWBjZfC8lzeqlBkK9HM2s2Izm0YQL7zk7g36Ged1oBrWoD0OXBZTY1iX7ZQADwJ/cvcFmcpfpmSqnO5+PNANaAYclaHsZUS6ZTSzIUB/d38y03nLpAx9lt9x968Bh4d/Z2cqf5mSgXKWELRueNvd9wfeBXKuiU2mvpuh0wl+h3JKBr6bTQgC1f2A7sDHwJUZzaTkk3g31nLu5kwWHBr+lo0CLgqbjUrh+RvQDxgCLCe4eV5QMnzeywtxylywn7O7l7n7EIJrsAPNrEG70eVtoBpe/DwO/NfdnwgXrwjbk0fala9McnPjgXnufnvGM5qmDJcTd98GPE0ONbXKUBmHAQeE/YDeAvYI+3LmjEx9lu6+NPy/EXiAoCldzshQOdcQ1IxHbjw8CuyfhezWWSa/m2a2L1Di7lOyktk6ylAZhwC4+2dhbfEjwCHZybHkgSVAr5jnPWkETcHdfVn4fyXB71pO/W5nUZ2vV/KRu68IL/TLgbspsM8509ek+SBemQv9cwZw93XA68BIGvAzzstANWzq+A9gtrv/Mealp4FzwsfnAE8lsa0bgV2AyzKczbRlqpxm1jrmACsBRgNzMp/j1GWqjO7+N3fv7u59gMOAT919eOZzXDcZ/CxLLBwxNfzxPAHImRGOM/h5OvAMMDxcdDSQM4OtZPI3KHQGOVabmsEyLgUGm1nn8PmxBP18pHH6EBhgZn0tGMTwdIJjqmCZWatwIBbCQceOI4d+t7Osrr+JeSlyrRU6hQL6nLNw3st5icpcqJ+zmXWOdL0ysxbAMQTxQoN9xvk66u9hBAOOzADKw8VXEbQbfwToDSwCTnP3teE6CwkG8WgKrCM4UWwg6CszB9gebucv7n5PfZSjNhks5xrgWYImv8XAqwQji5bWU1ESylQZPWbEyLAfwbOeW6P+Zuqz/AKYRNDBvRh4GfiZu5fVU1FqlMnP08x2A/4NtANWAee5+6L6KktNMn3cmtkCYLS758QNJMj4Z3khwciBOwmO4XPdfU29FUZyigVTOdxO8Bt2r7v/tmFzlF1mtjsVrUNKgAcKscxm9iDBzcVOwArg18D/SPB7ke8SlHc4QSsSBxYCP4z07ct3dTkn5LsaynwGBfg5m9k+BIMlFRNUZj7i7r8xs4400Gecl4GqiIiIiIiIFK68bPorIiIiIiIihUuBqoiIiIiIiOQUBaoiIiIiIiKSUxSoioiIiIiISE5RoCoiIiIiIiI5RYGqiIiIiIiI5BQFqiIiIiIiIpJT/h/cc4YBrkaLUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = model_fit.resid\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16,4))\n",
    "\n",
    "ax1.plot(residuals)\n",
    "ax1.set_title('Residuals')\n",
    "ax2.set_ylim(0,1)\n",
    "plot_acf(residuals, lags=30, ax=ax2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Close</td>      <th>  No. Observations:  </th>   <td>2011</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 1, 2)</td>  <th>  Log Likelihood     </th> <td>3516.372</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 03 Jul 2024</td> <th>  AIC                </th> <td>-7024.745</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:49:10</td>     <th>  BIC                </th> <td>-7002.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>-7016.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 2011</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.2497</td> <td>    0.330</td> <td>    0.756</td> <td> 0.450</td> <td>   -0.398</td> <td>    0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.2358</td> <td>    0.331</td> <td>   -0.712</td> <td> 0.477</td> <td>   -0.885</td> <td>    0.414</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.0476</td> <td>    0.017</td> <td>   -2.864</td> <td> 0.004</td> <td>   -0.080</td> <td>   -0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0018</td> <td> 2.79e-05</td> <td>   63.475</td> <td> 0.000</td> <td>    0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>3565.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.91</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>5.22</td> <th>  Skew:              </th>  <td>-0.46</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>9.46</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &      Close       & \\textbf{  No. Observations:  } &    2011     \\\\\n",
       "\\textbf{Model:}                  &  ARIMA(1, 1, 2)  & \\textbf{  Log Likelihood     } &  3516.372   \\\\\n",
       "\\textbf{Date:}                   & Wed, 03 Jul 2024 & \\textbf{  AIC                } & -7024.745   \\\\\n",
       "\\textbf{Time:}                   &     19:49:10     & \\textbf{  BIC                } & -7002.321   \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } & -7016.513   \\\\\n",
       "\\textbf{}                        &      - 2011      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ar.L1}  &       0.2497  &        0.330     &     0.756  &         0.450        &       -0.398    &        0.897     \\\\\n",
       "\\textbf{ma.L1}  &      -0.2358  &        0.331     &    -0.712  &         0.477        &       -0.885    &        0.414     \\\\\n",
       "\\textbf{ma.L2}  &      -0.0476  &        0.017     &    -2.864  &         0.004        &       -0.080    &       -0.015     \\\\\n",
       "\\textbf{sigma2} &       0.0018  &     2.79e-05     &    63.475  &         0.000        &        0.002    &        0.002     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.01 & \\textbf{  Jarque-Bera (JB):  } & 3565.65  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.91 & \\textbf{  Prob(JB):          } &   0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 5.22 & \\textbf{  Skew:              } &  -0.46   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &   9.46   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Close   No. Observations:                 2011\n",
       "Model:                 ARIMA(1, 1, 2)   Log Likelihood                3516.372\n",
       "Date:                Wed, 03 Jul 2024   AIC                          -7024.745\n",
       "Time:                        19:49:10   BIC                          -7002.321\n",
       "Sample:                             0   HQIC                         -7016.513\n",
       "                               - 2011                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.2497      0.330      0.756      0.450      -0.398       0.897\n",
       "ma.L1         -0.2358      0.331     -0.712      0.477      -0.885       0.414\n",
       "ma.L2         -0.0476      0.017     -2.864      0.004      -0.080      -0.015\n",
       "sigma2         0.0018   2.79e-05     63.475      0.000       0.002       0.002\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):              3565.65\n",
       "Prob(Q):                              0.91   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               5.22   Skew:                            -0.46\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.46\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARIMA(scaled_df, order=(1,1,2)) # order=(p,d,q)\n",
    "model_fit = model.fit()\n",
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "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": [
    "forecast_steps = 30\n",
    "forecast = model_fit.forecast(steps=forecast_steps)\n",
    "forecast_values = scaler.inverse_transform(forecast.values.reshape(-1,1))\n",
    "\n",
    "forecasts_dates = pd.date_range(\n",
    "    start = dt.index[-1] + pd.Timedelta(days=1),\n",
    "    periods=forecast_steps,\n",
    "    freq='D'\n",
    ")\n",
    "forecasts_df = pd.DataFrame(forecast_values, index=forecasts_dates, columns=['Forecast'])\n",
    "\n",
    "# Plotting forecast\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(dt.index, dt['Close'], label='Original')\n",
    "plt.plot(forecasts_df.index, forecasts_df['Forecast'], label='Forecast')\n",
    "plt.title('Forecast')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using pmdarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pmdarima as pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=-6437.327, Time=0.35 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=-6438.358, Time=0.12 sec\n",
      " ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=-6438.867, Time=0.29 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=-6439.027, Time=0.37 sec\n",
      " ARIMA(0,1,0)(0,0,0)[0]             : AIC=-6436.516, Time=0.16 sec\n",
      " ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=-6438.271, Time=0.49 sec\n",
      " ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=-6436.131, Time=0.32 sec\n",
      " ARIMA(0,1,1)(0,0,0)[0]             : AIC=-6437.484, Time=0.09 sec\n",
      "\n",
      "Best model:  ARIMA(0,1,1)(0,0,0)[0] intercept\n",
      "Total fit time: 2.211 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>   <td>1608</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 1)</td> <th>  Log Likelihood     </th> <td>3222.513</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 03 Jul 2024</td> <th>  AIC                </th> <td>-6439.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:49:13</td>     <th>  BIC                </th> <td>-6422.881</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>-6433.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 1608</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>    0.0016</td> <td>    0.001</td> <td>    1.875</td> <td> 0.061</td> <td>-7.26e-05</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>    0.0420</td> <td>    0.019</td> <td>    2.205</td> <td> 0.027</td> <td>    0.005</td> <td>    0.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>    0.0011</td> <td>  2.2e-05</td> <td>   48.246</td> <td> 0.000</td> <td>    0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>964.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.96</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.79</td> <th>  Skew:              </th>  <td>-0.02</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>6.79</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &        y         & \\textbf{  No. Observations:  } &    1608     \\\\\n",
       "\\textbf{Model:}                  & SARIMAX(0, 1, 1) & \\textbf{  Log Likelihood     } &  3222.513   \\\\\n",
       "\\textbf{Date:}                   & Wed, 03 Jul 2024 & \\textbf{  AIC                } & -6439.027   \\\\\n",
       "\\textbf{Time:}                   &     19:49:13     & \\textbf{  BIC                } & -6422.881   \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } & -6433.033   \\\\\n",
       "\\textbf{}                        &      - 1608      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{intercept} &       0.0016  &        0.001     &     1.875  &         0.061        &    -7.26e-05    &        0.003     \\\\\n",
       "\\textbf{ma.L1}     &       0.0420  &        0.019     &     2.205  &         0.027        &        0.005    &        0.079     \\\\\n",
       "\\textbf{sigma2}    &       0.0011  &      2.2e-05     &    48.246  &         0.000        &        0.001    &        0.001     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.00 & \\textbf{  Jarque-Bera (JB):  } & 964.38  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.96 & \\textbf{  Prob(JB):          } &  0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 1.79 & \\textbf{  Skew:              } & -0.02   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &  6.79   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   No. Observations:                 1608\n",
       "Model:               SARIMAX(0, 1, 1)   Log Likelihood                3222.513\n",
       "Date:                Wed, 03 Jul 2024   AIC                          -6439.027\n",
       "Time:                        19:49:13   BIC                          -6422.881\n",
       "Sample:                             0   HQIC                         -6433.033\n",
       "                               - 1608                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.0016      0.001      1.875      0.061   -7.26e-05       0.003\n",
       "ma.L1          0.0420      0.019      2.205      0.027       0.005       0.079\n",
       "sigma2         0.0011    2.2e-05     48.246      0.000       0.001       0.001\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               964.38\n",
       "Prob(Q):                              0.96   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.79   Skew:                            -0.02\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         6.79\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Performing grid search for ARIMA\n",
    "\n",
    "optimized_model = pm.auto_arima(\n",
    "    train,\n",
    "    start_p=1,\n",
    "    start_q=1,\n",
    "    test='adf', # using adf test to find optimal 'd'\n",
    "    max_p=5,\n",
    "    max_q=5,\n",
    "    m=1,\n",
    "    start_P=0,\n",
    "    seasonal=False,\n",
    "    d=None,\n",
    "    D=0,\n",
    "    trace=True,\n",
    "    error_action='ignore',\n",
    "    suppress_warnings=True,\n",
    "    stepwise=True\n",
    ")\n",
    "\n",
    "optimized_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best ARIMA order (p,d,q): (0, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "order = optimized_model.order\n",
    "print(f'Best ARIMA order (p,d,q): {order}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building Model using Optimized ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Close</td>      <th>  No. Observations:  </th>   <td>2011</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(0, 1, 1)</td>  <th>  Log Likelihood     </th> <td>3514.047</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 03 Jul 2024</td> <th>  AIC                </th> <td>-7024.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:49:13</td>     <th>  BIC                </th> <td>-7012.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>-7019.978</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 2011</td>     <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>    0.0138</td> <td>    0.015</td> <td>    0.927</td> <td> 0.354</td> <td>   -0.015</td> <td>    0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0018</td> <td> 2.73e-05</td> <td>   65.021</td> <td> 0.000</td> <td>    0.002</td> <td>    0.002</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>3673.16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.93</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>5.23</td> <th>  Skew:              </th>  <td>-0.44</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>9.56</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}          &      Close       & \\textbf{  No. Observations:  } &    2011     \\\\\n",
       "\\textbf{Model:}                  &  ARIMA(0, 1, 1)  & \\textbf{  Log Likelihood     } &  3514.047   \\\\\n",
       "\\textbf{Date:}                   & Wed, 03 Jul 2024 & \\textbf{  AIC                } & -7024.094   \\\\\n",
       "\\textbf{Time:}                   &     19:49:13     & \\textbf{  BIC                } & -7012.882   \\\\\n",
       "\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } & -7019.978   \\\\\n",
       "\\textbf{}                        &      - 2011      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{ma.L1}  &       0.0138  &        0.015     &     0.927  &         0.354        &       -0.015    &        0.043     \\\\\n",
       "\\textbf{sigma2} &       0.0018  &     2.73e-05     &    65.021  &         0.000        &        0.002    &        0.002     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Ljung-Box (L1) (Q):}     & 0.01 & \\textbf{  Jarque-Bera (JB):  } & 3673.16  \\\\\n",
       "\\textbf{Prob(Q):}                & 0.93 & \\textbf{  Prob(JB):          } &   0.00   \\\\\n",
       "\\textbf{Heteroskedasticity (H):} & 5.23 & \\textbf{  Skew:              } &  -0.44   \\\\\n",
       "\\textbf{Prob(H) (two-sided):}    & 0.00 & \\textbf{  Kurtosis:          } &   9.56   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{SARIMAX Results}\n",
       "\\end{center}\n",
       "\n",
       "Warnings: \\newline\n",
       " [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                  Close   No. Observations:                 2011\n",
       "Model:                 ARIMA(0, 1, 1)   Log Likelihood                3514.047\n",
       "Date:                Wed, 03 Jul 2024   AIC                          -7024.094\n",
       "Time:                        19:49:13   BIC                          -7012.882\n",
       "Sample:                             0   HQIC                         -7019.978\n",
       "                               - 2011                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1          0.0138      0.015      0.927      0.354      -0.015       0.043\n",
       "sigma2         0.0018   2.73e-05     65.021      0.000       0.002       0.002\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):              3673.16\n",
       "Prob(Q):                              0.93   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               5.23   Skew:                            -0.44\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.56\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_model = ARIMA(scaled_df, order=(0,1,1)) # order=(p,d,q)\n",
    "final_model_fit = final_model.fit()\n",
    "final_model_fit.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Making Forecasts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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": [
    "forecast_steps = 30\n",
    "forecast = final_model_fit.forecast(steps=forecast_steps)\n",
    "forecast_values = scaler.inverse_transform(forecast.values.reshape(-1,1))\n",
    "\n",
    "forecasts_dates = pd.date_range(\n",
    "    start = dt.index[-1] + pd.Timedelta(days=1),\n",
    "    periods=forecast_steps,\n",
    "    freq='D'\n",
    ")\n",
    "forecasts_df = pd.DataFrame(forecast_values, index=forecasts_dates, columns=['Forecast'])\n",
    "\n",
    "# Plotting forecast\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(dt.index, dt['Close'], label='Original')\n",
    "plt.plot(forecasts_df.index, forecasts_df['Forecast'], label='Forecast')\n",
    "plt.title('Forecast')\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
}