{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63f6241d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import yfinance\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48729189",
   "metadata": {},
   "source": [
    "# Getting Crypto Data\n",
    "We used yahoo finance for data in this way later on we can again train our model with some newer data based on which are predictions can be more better.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "115b9d99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "df = yfinance.download('BTC-USD',start = '2017-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "fa66c8c2",
   "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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-10-21 00:00:00+00:00</th>\n",
       "      <td>19053.203125</td>\n",
       "      <td>19237.384766</td>\n",
       "      <td>18770.970703</td>\n",
       "      <td>19172.468750</td>\n",
       "      <td>19172.468750</td>\n",
       "      <td>32459287866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-22 00:00:00+00:00</th>\n",
       "      <td>19172.380859</td>\n",
       "      <td>19248.068359</td>\n",
       "      <td>19132.244141</td>\n",
       "      <td>19208.189453</td>\n",
       "      <td>19208.189453</td>\n",
       "      <td>16104440957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-23 00:00:00+00:00</th>\n",
       "      <td>19207.734375</td>\n",
       "      <td>19646.652344</td>\n",
       "      <td>19124.197266</td>\n",
       "      <td>19567.007812</td>\n",
       "      <td>19567.007812</td>\n",
       "      <td>22128794335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-24 00:00:00+00:00</th>\n",
       "      <td>19567.769531</td>\n",
       "      <td>19589.125000</td>\n",
       "      <td>19206.324219</td>\n",
       "      <td>19345.572266</td>\n",
       "      <td>19345.572266</td>\n",
       "      <td>30202235805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-25 00:00:00+00:00</th>\n",
       "      <td>19336.589844</td>\n",
       "      <td>19527.873047</td>\n",
       "      <td>19261.447266</td>\n",
       "      <td>19527.873047</td>\n",
       "      <td>19527.873047</td>\n",
       "      <td>24982974464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Open          High           Low  \\\n",
       "Date                                                                  \n",
       "2022-10-21 00:00:00+00:00  19053.203125  19237.384766  18770.970703   \n",
       "2022-10-22 00:00:00+00:00  19172.380859  19248.068359  19132.244141   \n",
       "2022-10-23 00:00:00+00:00  19207.734375  19646.652344  19124.197266   \n",
       "2022-10-24 00:00:00+00:00  19567.769531  19589.125000  19206.324219   \n",
       "2022-10-25 00:00:00+00:00  19336.589844  19527.873047  19261.447266   \n",
       "\n",
       "                                  Close     Adj Close       Volume  \n",
       "Date                                                                \n",
       "2022-10-21 00:00:00+00:00  19172.468750  19172.468750  32459287866  \n",
       "2022-10-22 00:00:00+00:00  19208.189453  19208.189453  16104440957  \n",
       "2022-10-23 00:00:00+00:00  19567.007812  19567.007812  22128794335  \n",
       "2022-10-24 00:00:00+00:00  19345.572266  19345.572266  30202235805  \n",
       "2022-10-25 00:00:00+00:00  19527.873047  19527.873047  24982974464  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e6ca6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afad1328",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35f92af7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         datetime64[ns, UTC]\n",
       "Open                     float64\n",
       "High                     float64\n",
       "Low                      float64\n",
       "Close                    float64\n",
       "Adj Close                float64\n",
       "Volume                     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f9b5c2",
   "metadata": {},
   "source": [
    "# Visualization of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6248d580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b926ab83d0>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df['Open'],color = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f2f5395",
   "metadata": {},
   "source": [
    "# Lets do some EDA Before Jumping into Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "78cf612c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['Date','Open','Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66623409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date     datetime64[ns, UTC]\n",
       "Open                 float64\n",
       "Close                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b3bccc5",
   "metadata": {},
   "source": [
    "# SIMPLE MOVING AVERAGES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3057b595",
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01 00:00:00+00:00</td>\n",
       "      <td>963.658020</td>\n",
       "      <td>998.325012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02 00:00:00+00:00</td>\n",
       "      <td>998.617004</td>\n",
       "      <td>1021.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03 00:00:00+00:00</td>\n",
       "      <td>1021.599976</td>\n",
       "      <td>1043.839966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04 00:00:00+00:00</td>\n",
       "      <td>1044.400024</td>\n",
       "      <td>1154.729980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05 00:00:00+00:00</td>\n",
       "      <td>1156.729980</td>\n",
       "      <td>1013.380005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Date         Open        Close\n",
       "0 2017-01-01 00:00:00+00:00   963.658020   998.325012\n",
       "1 2017-01-02 00:00:00+00:00   998.617004  1021.750000\n",
       "2 2017-01-03 00:00:00+00:00  1021.599976  1043.839966\n",
       "3 2017-01-04 00:00:00+00:00  1044.400024  1154.729980\n",
       "4 2017-01-05 00:00:00+00:00  1156.729980  1013.380005"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a8fd4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price: 10 days rolling'] = df['Close'].rolling(window = 10,min_periods = 1).mean()\n",
    "df['Price: 30 days rolling'] = df['Close'].rolling(window = 30,min_periods = 1).mean()\n",
    "df['Price: 50 days rolling'] = df['Close'].rolling(window = 50,min_periods = 1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7b5b481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Price: 10 days rolling','Price: 30 days rolling','Price: 50 days rolling']].plot(figsize = (20,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "befdcaa4",
   "metadata": {},
   "source": [
    "# Cummulative Moving Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8cf323c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Close'].expanding().mean().plot(figsize = (10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979af975",
   "metadata": {},
   "source": [
    "# EWMA (Exponential Weighted Moving Average)\n",
    "((Price- previous Ema) * Multiplier) + Prev EMA\n",
    "\n",
    "This will be for 10days \n",
    "\n",
    "Multiplier Formula = (2/(10+1)) = 0.181818 if i have a rolling window size of 10 days\n",
    "\n",
    "In Below code alpha(α) is our smoothning factor\n",
    "and span = α=2/(span+1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bc7d4348",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['EWM'] = df['Close'].ewm(span = 10).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80e93036",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# EWM With Respect to 10 Days\n",
    "df[['Close','EWM']].plot(figsize = (10,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e60bb2e",
   "metadata": {},
   "source": [
    "\n",
    "# Lets start Preprocessing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a466d298",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Price: 10 days rolling</th>\n",
       "      <th>Price: 30 days rolling</th>\n",
       "      <th>Price: 50 days rolling</th>\n",
       "      <th>EWM</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01 00:00:00+00:00</th>\n",
       "      <td>963.658020</td>\n",
       "      <td>998.325012</td>\n",
       "      <td>998.325012</td>\n",
       "      <td>998.325012</td>\n",
       "      <td>998.325012</td>\n",
       "      <td>998.325012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02 00:00:00+00:00</th>\n",
       "      <td>998.617004</td>\n",
       "      <td>1021.750000</td>\n",
       "      <td>1010.037506</td>\n",
       "      <td>1010.037506</td>\n",
       "      <td>1010.037506</td>\n",
       "      <td>1011.208755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03 00:00:00+00:00</th>\n",
       "      <td>1021.599976</td>\n",
       "      <td>1043.839966</td>\n",
       "      <td>1021.304993</td>\n",
       "      <td>1021.304993</td>\n",
       "      <td>1021.304993</td>\n",
       "      <td>1024.326285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04 00:00:00+00:00</th>\n",
       "      <td>1044.400024</td>\n",
       "      <td>1154.729980</td>\n",
       "      <td>1054.661240</td>\n",
       "      <td>1054.661240</td>\n",
       "      <td>1054.661240</td>\n",
       "      <td>1067.288493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 00:00:00+00:00</th>\n",
       "      <td>1156.729980</td>\n",
       "      <td>1013.380005</td>\n",
       "      <td>1046.404993</td>\n",
       "      <td>1046.404993</td>\n",
       "      <td>1046.404993</td>\n",
       "      <td>1051.812832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Open        Close  Price: 10 days rolling  \\\n",
       "Date                                                                          \n",
       "2017-01-01 00:00:00+00:00   963.658020   998.325012              998.325012   \n",
       "2017-01-02 00:00:00+00:00   998.617004  1021.750000             1010.037506   \n",
       "2017-01-03 00:00:00+00:00  1021.599976  1043.839966             1021.304993   \n",
       "2017-01-04 00:00:00+00:00  1044.400024  1154.729980             1054.661240   \n",
       "2017-01-05 00:00:00+00:00  1156.729980  1013.380005             1046.404993   \n",
       "\n",
       "                           Price: 30 days rolling  Price: 50 days rolling  \\\n",
       "Date                                                                        \n",
       "2017-01-01 00:00:00+00:00              998.325012              998.325012   \n",
       "2017-01-02 00:00:00+00:00             1010.037506             1010.037506   \n",
       "2017-01-03 00:00:00+00:00             1021.304993             1021.304993   \n",
       "2017-01-04 00:00:00+00:00             1054.661240             1054.661240   \n",
       "2017-01-05 00:00:00+00:00             1046.404993             1046.404993   \n",
       "\n",
       "                                   EWM  \n",
       "Date                                    \n",
       "2017-01-01 00:00:00+00:00   998.325012  \n",
       "2017-01-02 00:00:00+00:00  1011.208755  \n",
       "2017-01-03 00:00:00+00:00  1024.326285  \n",
       "2017-01-04 00:00:00+00:00  1067.288493  \n",
       "2017-01-05 00:00:00+00:00  1051.812832  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('Date')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d41ef873",
   "metadata": {},
   "source": [
    "# (ARIMA) Autoregressive integrated moving average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "55c45c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Adffuller\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "def adf_test(series):\n",
    "    result = adfuller(series)\n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Rejected Null hypothesis, indicating its stationary\")\n",
    "    elif result[1] >= 0.05:\n",
    "        print(\"Weak Evidence Against Null Hypothesis, indicating its non-stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1900defb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weak Evidence Against Null Hypothesis, indicating its non-stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "17d67aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating Differences\n",
    "df['First Difference'] =  df['Close'] - df['Close'].shift(1)\n",
    "df['Second Difference'] =  df['Close'] - df['Close'].shift(2)\n",
    "df['12 Difference'] =  df['Close'] - df['Close'].shift(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "41a2a582",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c95010c7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Price: 30 days rolling</th>\n",
       "      <th>Price: 50 days rolling</th>\n",
       "      <th>EWM</th>\n",
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       "      <th>12 Difference</th>\n",
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       "    <tr>\n",
       "      <th>2017-01-14 00:00:00+00:00</th>\n",
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       "      <td>-2.186035</td>\n",
       "      <td>-222.041992</td>\n",
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       "    <tr>\n",
       "      <th>2017-01-16 00:00:00+00:00</th>\n",
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       "    <tr>\n",
       "      <th>2017-01-17 00:00:00+00:00</th>\n",
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       "      <td>850.796295</td>\n",
       "      <td>914.751407</td>\n",
       "      <td>914.751407</td>\n",
       "      <td>862.872134</td>\n",
       "      <td>76.403992</td>\n",
       "      <td>86.140015</td>\n",
       "      <td>-105.442017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open       Close  Price: 10 days rolling  \\\n",
       "Date                                                                        \n",
       "2017-01-13 00:00:00+00:00  803.737000  823.984009              910.717700   \n",
       "2017-01-14 00:00:00+00:00  825.142029  818.411987              877.085901   \n",
       "2017-01-15 00:00:00+00:00  818.142029  821.797974              857.927698   \n",
       "2017-01-16 00:00:00+00:00  821.783020  831.533997              850.860999   \n",
       "2017-01-17 00:00:00+00:00  830.945984  907.937988              850.796295   \n",
       "\n",
       "                           Price: 30 days rolling  Price: 50 days rolling  \\\n",
       "Date                                                                        \n",
       "2017-01-13 00:00:00+00:00              936.237845              936.237845   \n",
       "2017-01-14 00:00:00+00:00              927.821712              927.821712   \n",
       "2017-01-15 00:00:00+00:00              920.753463              920.753463   \n",
       "2017-01-16 00:00:00+00:00              915.177246              915.177246   \n",
       "2017-01-17 00:00:00+00:00              914.751407              914.751407   \n",
       "\n",
       "                                  EWM  First Difference  Second Difference  \\\n",
       "Date                                                                         \n",
       "2017-01-13 00:00:00+00:00  877.072088         19.150024          46.226990   \n",
       "2017-01-14 00:00:00+00:00  865.722922         -5.572021          13.578003   \n",
       "2017-01-15 00:00:00+00:00  857.322523          3.385986          -2.186035   \n",
       "2017-01-16 00:00:00+00:00  852.436668          9.736023          13.122009   \n",
       "2017-01-17 00:00:00+00:00  862.872134         76.403992          86.140015   \n",
       "\n",
       "                           12 Difference  \n",
       "Date                                      \n",
       "2017-01-13 00:00:00+00:00    -174.341003  \n",
       "2017-01-14 00:00:00+00:00    -203.338013  \n",
       "2017-01-15 00:00:00+00:00    -222.041992  \n",
       "2017-01-16 00:00:00+00:00    -323.195984  \n",
       "2017-01-17 00:00:00+00:00    -105.442017  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5db3ed9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rejected Null hypothesis, indicating its stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df['First Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4e54765",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rejected Null hypothesis, indicating its stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df['Second Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dca5116e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rejected Null hypothesis, indicating its stationary\n"
     ]
    }
   ],
   "source": [
    "adf_test(df['12 Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "decc8c79",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>76.403992</td>\n",
       "      <td>86.140015</td>\n",
       "      <td>-105.442017</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                 Open       Close  Price: 10 days rolling  \\\n",
       "Date                                                                        \n",
       "2017-01-13 00:00:00+00:00  803.737000  823.984009              910.717700   \n",
       "2017-01-14 00:00:00+00:00  825.142029  818.411987              877.085901   \n",
       "2017-01-15 00:00:00+00:00  818.142029  821.797974              857.927698   \n",
       "2017-01-16 00:00:00+00:00  821.783020  831.533997              850.860999   \n",
       "2017-01-17 00:00:00+00:00  830.945984  907.937988              850.796295   \n",
       "\n",
       "                           Price: 30 days rolling  Price: 50 days rolling  \\\n",
       "Date                                                                        \n",
       "2017-01-13 00:00:00+00:00              936.237845              936.237845   \n",
       "2017-01-14 00:00:00+00:00              927.821712              927.821712   \n",
       "2017-01-15 00:00:00+00:00              920.753463              920.753463   \n",
       "2017-01-16 00:00:00+00:00              915.177246              915.177246   \n",
       "2017-01-17 00:00:00+00:00              914.751407              914.751407   \n",
       "\n",
       "                                  EWM  First Difference  Second Difference  \\\n",
       "Date                                                                         \n",
       "2017-01-13 00:00:00+00:00  877.072088         19.150024          46.226990   \n",
       "2017-01-14 00:00:00+00:00  865.722922         -5.572021          13.578003   \n",
       "2017-01-15 00:00:00+00:00  857.322523          3.385986          -2.186035   \n",
       "2017-01-16 00:00:00+00:00  852.436668          9.736023          13.122009   \n",
       "2017-01-17 00:00:00+00:00  862.872134         76.403992          86.140015   \n",
       "\n",
       "                           12 Difference  \n",
       "Date                                      \n",
       "2017-01-13 00:00:00+00:00    -174.341003  \n",
       "2017-01-14 00:00:00+00:00    -203.338013  \n",
       "2017-01-15 00:00:00+00:00    -222.041992  \n",
       "2017-01-16 00:00:00+00:00    -323.195984  \n",
       "2017-01-17 00:00:00+00:00    -105.442017  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f9a46336",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df[['Close','EWM','First Difference','Second Difference','12 Difference']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2f6023c2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.plot(figsize = (10,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4de199c2",
   "metadata": {},
   "source": [
    "# ACF And PACF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "115435c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e543eaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5/XedTuvscaxvrz6Wzq7gk//wDD/bto+OZD2eNXkUn7nqtfU4euggWgY3cfho1/HhWgY3MXLooOM730fX7Si5c35sXTuTTxla/XxFLqblG3cfXzZvmTW227IZ2VI6nhEtg3h2y6tVTSPvQMcxAFZt3lO2DhRv/TB97DDGj2ypap5qkflEsO/wUba+erjq+l1dwcpNe3hp5wFmnzoid2RUsLF0dQWf+9FaNrTv58ixLoYMamLuxJF84vL5NDWJFRt3s/rlvXQcy21Mh452sfrlvfzLs9s4b9bY1KaTtiOduXi37+04od+KjbtZUzRPa7bu5eE122uap2qs2LibNVtfm9bhZFrL1rZ3m1ZfSWOeqvHxS8/khvtX0XG0k2vfNocFM8aw52BtBzBpbzPDW5oZ3tLMGZNGs/fQsZqGXbFxN2u37uu2Htdu3ce/rttxfD3OmziK0yeMZM3WvURAy6AmTp8wkjMmjuLV5OBtyuihDBnUdHw8AEMGNTF59LCaYqpm2Zw5qXQ8Z04axb7Dtc1/Z7LHP9DRWbJ/ue2qt85+U0kEki4D/obc/yz+WkTcXtS/BfgW8BZgJ/DBiHgp6XcTcB3QCfxhRDyYRky9oZqNZeWmPWxo3398w+w41sWG9v2s3LSH82aN5aWdBzhSsNECHDnWxUs7Dxz/AaQxnb6U1jylNa2+Uu08pZEsmprEqKGDGDV00EnP50DbZpqaxCcun39CAixcdgtmjGHuxO4757kTR7Jgxpia4qlm2VQTTxoqbVe9pe5rBJKaga8AlwNnA9dIOruo2nXA7oiYC9wB/Hky7NnA1cA5wGXA/03G15AKN5ag+8aSV2kDB5h96giGDOq+2IcMamL2qSNSnU5fSmue0poW5H5MKzbu5v4Vm1mxcTddvXAkVc085X/UX3pkPfct38yXHlnP5360tlfi6UlfbzOV1kG16zGfAMePauG8WWNP2Onmd87Txgxjwsgh/OG7553UGU61y6aneNKQ1m+lFqrULlzVCKQLgVsj4r1J900AEfH5gjoPJnV+ImkQsA2YANxYWLewXqVpjps1Py75xJKaY12zNdeuePaU1y6ydRzr7HZaWcmOfR28sv/ICeUTRg5h/Khcu92+w8fYsudQt7Y+CaaNGcaooYOICH6+69DxOw+kXPv7zHHDkJTadNK2cedBAGadOvyEfmnNUzWqmVa+zqGjnUSUr7O/o5PDRzsZOriZkS3Nx/tVq6/XU6V1UI20t5lqtoly66Ca9VjNdGqpU0kty6beafU0jkrb1YxxwxncfPLH7/d+5G3LI6K1uDyNPcY0YFNB92bggnJ1IuKYpFeBU5PyJ4qGnVZqIpIWAYsARk45/aQCLUwApUQEL75ykK4IJo0eesLOYejgZiRO2FhaBr92EjOyJXdRtfgHMLKlOT8fzBw3jP0dnXQc7aSlxE4ojekUSuOHVGnYtOapmliqmdb+js7jywVy0zx0tJP9HZ1VJ+PccPVvD4cL4nhtvNBxtLPmHUxPO56e4k17m6nUr6d1UM16rHa+q61TaZ5qWTY9TaveZVfLbyUtA+ZicUQsBhYDtLa2xvd+78JUxrtx5wFe3nP4+Cn8kc4uInJZ+ZRh3dt7+6pNOO3p3PZPqwG45VfOKTu9G+5fxeGjnfzKG6f2ebtn8bR6ircn96/YzH3LN3cvDLjwtFN533nTWbFxN196ZP1rvZI7Rn71TdNOuKZR7/aQn1bhWWfLoCaufducE9rl65nvauLN10tjm+lJT+ugP1TzO0jjwn+9y67SdjV30kgmja7+bqhi936kdHkaiWALMKOge3pSVqrO5qRp6BRyF42rGbZP5Nvl8lm40sWinjaWpiZx3qyxJ30BLs3pdHUF+w4f4/DRTlZs3F327qP8afGXHlnfK3cfVTtPaci3PxffSZJvf67mQmVa20P+Ymbxj7r4YmZP66kn1cSbj7nebaYaPa2DvlbNPNX7u01LX/5W8tJIBE8B8yTNIbcTvxr4zaI6S4GFwE+A9wOPRERIWgp8V9JfA1OBecB/phBTzaq9G6WvNpY0plPNTr7aHUga+mrZ9bTzrWYnldb2UM2POo1knNbdVGkdGFSbAPtCXx3spKmvk1LdiSBp878eeJDc7aNLImK1pNuAtohYCnwduEvSBmAXuWRBUu9eYA1wDPiDiCh9Y20vGTa4mTHDB3PO1NH846oTHxY5Z+poxgwf3JchneBkr+c/+eJOnt/RfSf//I79PNe+jwvmnArA1r2HS+5Atu09xOhhE3olrko6u4IDHceS+8r3ct7MsWUfeIoTHrd5zWeuegMrNu7mhVcOcNr4EZxX8HDQ2+aeyoNrtvHc9tceZjpj0ijeNve1B/LOmjyq5MNDZ00exYgS7cY9eecZ43kn40v2e+rFXSXX08+27eWtc8ZVtZzPKBPvGZNHnfBUciX5h82KY1mzbS9vnT2u4kOHefkat15xDj/9+W5efOUAc8aP4NxK67JXbqLKjbTtpT08X3Sw83z7fp59+VVaZ49NffoiN4+DmrvPa6lpFC/P4iq9s1xOlMo1goh4AHigqOyWgu+Hgd8oM+xngc+mEcfJmDh6KBNHD+WMSaP48ZrtJ7yz5HcunD1gn1h9eO12Oo5238l3HO1i/+Fjx19P8K4zJ3D/is0nvDfmojMncs7UU/o03vx7YzbvPkRXwF/9eF1d7405d2b5o6kf/P7beWxdO2te3svZJV5Uds7UU1j2s/YTtodr3z6nF95hs6Pkejp4pJM3TR9T1TjeMO0UHi0R73+vMd7Hnysdy+EjnSd1NP/W2eNqHiZt/3/DzhPuDOw41sWRY128ZVb68eVvAmiEea/WgLlY3Nvy79SptHMYaKp5OVj+vTHFO5D+eKlc/r0x+dvNe/Mtm81N4j3zJ5Udb19uD2m8xC2teF+Pb5vty3nq7Ap2HzzCwY5Olq3dPmD2IU4EBXraOQw01ezkGykBNtpbNvtqe0grGacRbyMdGKSlr+Ypf0a7oX0/XQEfvfunA+Yf9tT9QFl/aG1tjba2tv4OY0DIv6u9v3fy1Vi2djsfvfun3Y7chg9p5svXnPu6Sc7lNNJ6aqRY0tIX8zQQtl9JvfZAmTWwgXSW83o8Gq1WI62nRoolLX0xT412RlsLJwJrGI3UTGVWq4F8fcWJwBrK6/Fo1LJhIJ/ROhGYmaVgIJ/ROhGYmaVkoJ7R+n8Wm5llnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcbVlQgkjZP0kKT1yd8T/jegpAWSfiJptaRVkj5Y0O+bkl6UtDL5LKgnHjMzq129ZwQ3AssiYh6wLOkudhD4UEScA1wGfFHSmIL+H4+IBclnZZ3xmJlZjepNBFcCdybf7wSuKq4QEc9FxPrk+8tAOzChzumamVlK6k0EkyJia/J9G1DxlXuSzgeGAM8XFH82aTK6Q1JLhWEXSWqT1LZjx446wzYzs7weE4GkhyU9W+JzZWG9yP3z47L/AFnSFOAu4Hcjoispvgk4C3grMA64odzwEbE4IlojonXCBJ9QmJmlpcf/RxARF5frJ2m7pCkRsTXZ0beXqTca+Gfg5oh4omDc+bOJDknfAP60pujNzKxu9TYNLQUWJt8XAj8sriBpCPAD4FsRcV9RvynJX5G7vvBsnfGYmVmN6k0EtwOXSFoPXJx0I6lV0teSOh8AfhG4tsRtot+R9AzwDDAe+Eyd8ZiZWY2Ua9ofWFpbW6Otra2/wzAzG1AkLY+I1uJyP1lsZpZxTgRmZhnnRGBmlnFOBGZmGedEYGaWcU4EZmYZ50RgZpZxTgRmZhnnRGBmlnFOBGZmGedEYGaWcU4EZmYZ50RgZpZxTgRmZhnnRGBmlnFOBGZmGedEYGaWcU4EZmYZV1cikDRO0kOS1id/x5ap11nw/4qXFpTPkfSkpA2Svpf8o3szM+tD9Z4R3Agsi4h5wLKku5RDEbEg+VxRUP7nwB0RMRfYDVxXZzxmZlajehPBlcCdyfc7gauqHVCSgHcD953M8GZmlo56E8GkiNiafN8GTCpTb6ikNklPSLoqKTsV2BMRx5LuzcC0chOStCgZR9uOHTvqDNvMzPIG9VRB0sPA5BK9bi7siIiQFGVGMysitkg6DXhE0jPAq7UEGhGLgcUAra2t5aZjZmY16jERRMTF5fpJ2i5pSkRslTQFaC8zji3J3xckPQacC3wfGCNpUHJWMB3YchLzYGZmdai3aWgpsDD5vhD4YXEFSWMltSTfxwNvB9ZERACPAu+vNLyZmfWuehPB7cAlktYDFyfdSGqV9LWkznygTdLT5Hb8t0fEmqTfDcDHJG0gd83g63XGY2ZmNVLuwHxgaW1tjba2tv4Ow8xsQJG0PCJai8v9ZLGZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWVcXYlA0jhJD0lan/wdW6LOuyStLPgclnRV0u+bkl4s6LegnnjMzKx29Z4R3Agsi4h5wLKku5uIeDQiFkTEAuDdwEHgxwVVPp7vHxEr64zHzMxqVG8iuBK4M/l+J3BVD/XfD/woIg7WOV0zM0tJvYlgUkRsTb5vAyb1UP9q4O6iss9KWiXpDkktdcZjZmY1GtRTBUkPA5NL9Lq5sCMiQlJUGM8U4I3AgwXFN5FLIEOAxcANwG1lhl8ELAKYOXNmT2GbmVmVekwEEXFxuX6StkuaEhFbkx19e4VRfQD4QUQcLRh3/myiQ9I3gD+tEMdicsmC1tbWsgnHzMxqU2/T0FJgYfJ9IfDDCnWvoahZKEkeSBK56wvP1hmPmZnVqN5EcDtwiaT1wMVJN5JaJX0tX0nSbGAG8K9Fw39H0jPAM8B44DN1xmNmZjXqsWmokojYCbynRHkb8OGC7peAaSXqvbue6ZuZWf38ZLGZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcY5EZiZZZwTgZlZxjkRmJllnBOBmVnGORGYmWWcE4GZWcbVlQgk/Yak1ZK6JLVWqHeZpHWSNki6saB8jqQnk/LvSRpSTzxmZla7es8IngXeBzxeroKkZuArwOXA2cA1ks5Oev85cEdEzAV2A9fVGY+ZmdWorkQQEWsjYl0P1c4HNkTECxFxBLgHuFKSgHcD9yX17gSuqiceMzOr3aA+mMY0YFNB92bgAuBUYE9EHCson1ZuJJIWAYuSzv2SekpA5YwHXjnJYfuD4+1djrf3DbSYX8/xzipV2GMikPQwMLlEr5sj4odVTrxuEbEYWFzveCS1RUTZ6xmNxvH2Lsfb+wZazFmMt8dEEBEX1zMBYAswo6B7elK2ExgjaVByVpAvNzOzPtQXt48+BcxL7hAaAlwNLI2IAB4F3p/UWwj02RmGmZnl1Hv76K9J2gxcCPyzpAeT8qmSHgBIjvavBx4E1gL3RsTqZBQ3AB+TtIHcNYOv1xNPlepuXupjjrd3Od7eN9Bizly8yh2Ym5lZVvnJYjOzjHMiMDPLuEwlgnKvumhUkl6S9IyklZLa+jueYpKWSGqX9GxB2ThJD0lan/wd258xFioT762StiTLeKWkX+7PGAtJmiHpUUlrkle5/FFS3pDLuEK8DbmMJQ2V9J+Snk7i/XRS3pCvvqkQ7zclvViwfBfUPO6sXCNIXnXxHHAJuYfXngKuiYg1/RpYBZJeAlojoiEfbpH0i8B+4FsR8Yak7C+AXRFxe5Jsx0bEDf0ZZ16ZeG8F9kfEX/VnbKVImgJMiYgVkkYBy8k9fX8tDbiMK8T7ARpwGSdvNxgREfslDQb+Hfgj4GPA/RFxj6S/A56OiL/tz1ihYrwfAf4pIu6rOIIKsnRGUPJVF/0c04AWEY8Du4qKryT3uhBosNeGlIm3YUXE1ohYkXzfR+6uu2k06DKuEG9Dipz9Sefg5BM06KtvKsRbtywlglKvumjYjTQRwI8lLU9esTEQTIqIrcn3bcCk/gymStdLWpU0HTVEM0sxSbOBc4EnGQDLuCheaNBlLKlZ0kqgHXgIeJ4aXn3T14rjjYj88v1ssnzvkNRS63izlAgGondExHnk3tz6B0nTxoCRPDTY6G2PfwucDiwAtgJf6NdoSpA0Evg+8McRsbewXyMu4xLxNuwyjojOiFhA7s0G5wNn9W9ElRXHK+kNwE3k4n4rMI7c81k1yVIiKPeqi4YVEVuSv+3AD8htqI1ue9JWnG8zbu/neCqKiO3Jj6sL+H802DJO2oK/D3wnIu5Piht2GZeKt9GXMUBE7CH3poMLSV59k/RqyP1EQbyXJU1yEREdwDc4ieWbpURQ8lUX/RxTWZJGJBfckDQCuJTc/39odEvJvS4EBsBrQ/I71MSv0UDLOLk4+HVgbUT8dUGvhlzG5eJt1GUsaYKkMcn3YeRuJFlLg776pky8Pys4KBC56xk1L9/M3DUEkNy29kWgGVgSEZ/t34jKk3QaubMAyL0c8LuNFq+ku4GLyL0GdzvwKeAfgHuBmcBG4AMR0RAXaMvEexG5JosAXgJ+r6D9vV9Jegfwb8AzQFdS/Aly7e4Nt4wrxHsNDbiMJb2J3MXgZnIHxfdGxG3Jb+8ecs0sPwV+Ozna7lcV4n0EmAAIWAl8pOCicnXjzlIiMDOzE2WpacjMzEpwIjAzyzgnAjOzjHMiMDPLOCcCM7OMcyIwM8s4JwIzs4z7LxWaqc6OJhNNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acf = plot_acf(new_df[\"First Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3242b5ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pacf = plot_pacf(new_df[\"First Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1b260022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Q = 4\n",
    "acf = plot_acf(new_df['12 Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7211f000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#P = 2\n",
    "pacf = plot_pacf(new_df[\"12 Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9e7045b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PnDCSeVPG8Py2/WQChg9tYt6UMcwYP5JN7Qf7GGOfJjtldWeCFa/uZVP7Ic6ePJp3nzlhwPZ4n3l5D6u27ONo3jq8ass+vvPsZn7xrNOPx3vLw2v42WsH6OjMMGxoE+eeMZYv/NbbaW5S0Xl8+5m35pEzfEgzw4c0M2P8SDb2Wp+e2rC74Ebz336+m6mnjaj7Z+/OBG2b9/LS7jc5e9IoWmdX9z2MGT6EYUObjn9ugGFDmxg9fAhrd7xRts7eN48xYVRL/T5QIvWJYO+bx9j7ZkeysQ8OdXQRAc+8vLdP81u1eR9rdxygoyv7JR7pzLB2xwF+9OJrXDB7PM9t2d/jCwY42pnhuS37mTNxdMl5PJrMYyAMb2lmeEszZ00azZ5Dx04Y/8kr5nHTwy/Q0dnN9b80h4Uzx7H3zRPrWfUymeALP1zPpvZDHOvK0DKkiXMmj+bTV82naQCSwZptb9DRax3u6MywZtsbzJ08Bsiuw+t3vrUOH+3MsH7nAX7ys3YumD2eF7cXnseL29/gbVPG9CjvzGTrFVqfzhg7nJYhTceXA9AypIkpY4bzxpHOqj5XJhOs3rqfV/e8yZmnj2LhzHE92rce38PbJo/h7EmjWbfzABEwbEgTZ08azdsmj+HAka6ydTq7M2WW0Depv7P44NFOdh88xv7DnRzq6CITQdD33dFX97zJsa6eX9axrszxPegzTx9Fy5Cezd4ypIkzTx9V8TwaUVOTGDN8CBPHDOOC2eMHZAN1qlq9dT+b2g/R0ZUhgI6uDJvaD7F66/4e9TKZYNXmfTy8ahurNu8jkzk5h1X1WIcrmUclFs4cxzmTR6NkdRuWbJwXzhxX1XxyG/mvPLmRh1Zu4ytPbuQLP1zfow0r/R5KaWoSn75qPtPHjWDS6Bb+6NK5JySSSurUW10SgaQrJW2QtEnSzQXGD5P0QDL+WUln5o27JSnfIOn99YjnZCr3Yyu3gley4lb6I+mvH/5g1F9t0x/LqWTHoJINWaXKfaZ6rMP12oDXa6NZyUa+0u+h3PpQyU5Tf+9Y1dw1JKkZ+CpwObANWCFpea9/Qn8DsC8izpF0LfBF4MOSzgOuBRYA04DHJb0tInp2+jWISg4Ncyt4/mFd/gqeW3F7d6Pkf9Hl5lFpLI2m3KF3vebTX23TX8vJbVR7d3/k7xjkb8ig54asmu7ESj5TPdbhSuZRqdxGc8zwIX3uOi21kc/Ns9z3MBh/kzmq9sThCTOQLgY+GxHvT4ZvAYiIv8yr82hS56eShgCvAZOAm/Pr5tcrtcwJs+fH5Z9eVnWs63YeAOC8qW9dzdLR1d3ji9285zAAs08fecL0B492sX3/kR4nMiWYPm4EY4a/lVMjgldeP0wmgiljhzN6WDNSzxWh1HIqmUelsVQiIjjU0c3Rzm6GD23uU7zl6kQEW/Ye4UhnNxHZWEcMbWbWhBEnLKtcrOXmU8+2KaWa9aFc+5aqk/vMuStjCn3m3Qc7eL3AuZtJo1uYOGZYxbFU03a1rsOVzKNederxuct9D/Vsu2J1RgxtZmhz3ztyHvzYL62MiNbe5fX4VUwHtuYNbwMuKlYnIrokvQGcnpQ/02va6YUWImkJsARg9NSz+xRofgIoptQXczTZ+OSLgI7O7h5ftCTOmlS6r7PUciqZR6WxQGUb6FIbmUriLVfnUEf38Y13LtYjnd0c6qgu3krmU2nb1Lohq2Q5lbRvuTqSmDVhBIc6uuno7GZYgQ3Z8KHNSJywERo2tLnH5ykXSzXrVa3rcCXzqEedSj736GHNjBjafMIOxuhhb7Vfue+hnm1XaZ16GTRXDUXEUmApQGtrazzwBxfXZb6b97zJjv1HK6q7avM+vvLkxh5HEMOGNHH9L83p96t5qonljn9ZC8Btv7Gg6HxyIrKXyP3mO6bX9TM9vGobD63c1rMw4OKzTucDF8yoON5K5lNJ22QywU0Pv8DRzm5+4+3TinYvHUsuFd59sIPTRvQ8zK9kOZW0bz2+g0q6JaqJpRHW8XqptH1r7brsj7Y7a9Iopowd3ufpH/xY4fJ6nCzeDszMG56RlBWsk3QNnQbsqXDahpHr9xw2pAnR9xNcjRRLf12hVM0J8INHu9h9sKNPJ+OhfNvkNprb9x/h9UPHSl4hktvDK3TysJLvoJL2rcd3kOtz/6NL5/LBd80oeNK0kuU00jpeL5W2b1OTuGD2eD5wwYw+naAdzG1XjyOCFcBcSXPIbsSvBX67V53lwGLgp8AHgScjIiQtB74r6W/JniyeC/xXHWI6KXI/tnqc8OyvWHIb1qOd3azavO+EOpWciKyH3I+k9x5roRPguX7Wrzy5sejJ+FLzKdc2pTbyuT23Sk4eVvIdVNK+9foOchuyYnuflSynkdbxeumvdXwwt13NiSDp878ReBRoBpZFxFpJdwBtEbEcuBu4V9ImYC/ZZEFS70FgHdAF/GF/XzGUPXE0hM5M9s7hru7SJ8/L/dj6U7lY6rVhrVes5X4klWygK/2xlWqbelwhUslyoLL27a/voNLlNNI6Xg/91b4weNuu5quGBkJra2u0tbWdlHkXerREVyayr+7M8XEno9nqPc9nX9nDF3/0sx53Mg8f2sRNV57LhXMmHC/rzgQrN+/j5d2HOGvSaN41e3zJ2+aribOauvev2MJ3n93S43Y+Ab990Sw+/O6ZxSar2opX9vLXP95wQrt86op5vDtpl+5M8Jnla/n5roPHH5HwtiljuP3qBUXbptRjR1Zt2ccrr7/JnImjuGDWie1bSZ166K/llNPXVb339qrcfHLVuzPBc1v2Hd95OH+APndp5Vtl5oSRTB7T93MEkk7aVUOnFEm0DBEtp8BN14+v31XwNv5DR7tYMO20HuXvmDGuHyMrrP3gZL7/3PYTHl536bmT6xrfgmmn8cTP2k944Nn175nTY+Pw/f/1Hp7a0M66HQc4r8YH/1Wyh9hfe5GDbW+1XvJ3fqwnJ4JT2GB7KmjuSae9N9D5Tzqth9xTLctt5JubxPvmT2nIp1ia1ZMTwSmsvzas9VLpBrpey/JG3izL5whOcblnwp/sDauZNT6fI0gp7/maWTmD/4yomZnVxInAzCzlnAjMzFLOicDMLOWcCMzMUs6JwMws5ZwIzMxSzonAzCzlnAjMzFLOicDMLOWcCMzMUs6JwMws5ZwIzMxSrqZEIGmCpMckbUz+nvCvjyQtlPRTSWslvSDpw3njvinpFUmrk9fCWuIxM7Pq1XpEcDPwRETMBZ5Ihns7DHwkIhYAVwJfljQub/ynImJh8lpdYzxmZlalWhPBIuCe5P09wDW9K0TEzyNiY/J+B9AOTKpxuWZmVie1JoIpEbEzef8aUPK/n0i6EGgBXsor/nzSZXSXpGElpl0iqU1S2+7du2sM28zMcsomAkmPS3qxwGtRfr3I/s/Lov/3UtJU4F7g9yMikxTfApwLvBuYANxUbPqIWBoRrRHROmmSDyjMzOql7L+qjIjLio2TtEvS1IjYmWzo24vUGwv8K3BrRDyTN+/c0USHpG8An6wqejMzq1mtXUPLgcXJ+8XAD3pXkNQCfB/4VkQ81Gvc1OSvyJ5feLHGeMzMrEq1JoI7gcslbQQuS4aR1Crp60mdDwG/Alxf4DLR70haA6wBJgKfqzEeMzOrkrJd+4NLa2trtLW1DXQYZmaDiqSVEdHau9x3FpuZpZwTgZlZyjkRmJmlnBOBmVnKORGYmaWcE4GZWco5EZiZpZwTgZlZyjkRmJmlnBOBmVnKORGYmaWcE4GZWco5EZiZpZwTgZlZyjkRmJmlnBOBmVnKORGYmaWcE4GZWcrVlAgkTZD0mKSNyd/xRep15/2/4uV55XMkPStpk6QHkn90b2Zm/ajWI4KbgSciYi7wRDJcyJGIWJi8rs4r/yJwV0ScA+wDbqgxHjMzq1KtiWARcE/y/h7gmkonlCTgUuChvkxvZmb1UWsimBIRO5P3rwFTitQbLqlN0jOSrknKTgf2R0RXMrwNmF5sQZKWJPNo2717d41hm5lZzpByFSQ9DpxRYNSt+QMREZKiyGxmR8R2SWcBT0paA7xRTaARsRRYCtDa2lpsOWZmVqWyiSAiLis2TtIuSVMjYqekqUB7kXlsT/6+LOkp4Hzge8A4SUOSo4IZwPY+fAYzM6tBrV1Dy4HFyfvFwA96V5A0XtKw5P1E4D3AuogI4CfAB0tNb2ZmJ1etieBO4HJJG4HLkmEktUr6elJnPtAm6XmyG/47I2JdMu4m4BOSNpE9Z3B3jfGYmVmVlN0xH1xaW1ujra1toMMwMxtUJK2MiNbe5b6z2Mws5ZwIzMxSzonAzCzlnAjMzFLOicDMLOWcCMzMUs6JwMws5ZwIzMxSzonAzCzlnAjMzFLOicDMLOWcCMzMUs6JwMws5ZwIzMxSzonAzCzlnAjMzFLOicDMLOWcCMzMUq6mRCBpgqTHJG1M/o4vUOfXJK3Oex2VdE0y7puSXskbt7CWeMzMrHq1HhHcDDwREXOBJ5LhHiLiJxGxMCIWApcCh4Ef51X5VG58RKyuMR4zM6tSrYlgEXBP8v4e4Joy9T8I/DAiDte4XDMzq5NaE8GUiNiZvH8NmFKm/rXAfb3KPi/pBUl3SRpWYzxmZlalIeUqSHocOKPAqFvzByIiJEWJ+UwF3g48mld8C9kE0gIsBW4C7igy/RJgCcCsWbPKhW1mZhUqmwgi4rJi4yTtkjQ1InYmG/r2ErP6EPD9iOjMm3fuaKJD0jeAT5aIYynZZEFra2vRhGNmZtWptWtoObA4eb8Y+EGJutfRq1soSR5IEtnzCy/WGI+ZmVWp1kRwJ3C5pI3AZckwklolfT1XSdKZwEzg33pN/x1Ja4A1wETgczXGY2ZmVSrbNVRKROwB3legvA34aN7wq8D0AvUurWX5ZmZWO99ZbGaWck4EZmYp50RgZpZyTgRmZinnRGBmlnJOBGZmKedEYGaWck4EZmYp50RgZpZyTgRmZinnRGBmlnJOBGZmKedEYGaWck4EZmYp50RgZpZyTgRmZinnRGBmlnJOBGZmKedEYGaWcjUlAkn/XdJaSRlJrSXqXSlpg6RNkm7OK58j6dmk/AFJLbXEY2Zm1av1iOBF4APA08UqSGoGvgpcBZwHXCfpvGT0F4G7IuIcYB9wQ43xmJlZlWpKBBGxPiI2lKl2IbApIl6OiGPA/cAiSQIuBR5K6t0DXFNLPGZmVr0h/bCM6cDWvOFtwEXA6cD+iOjKK59ebCaSlgBLksFDksoloGImAq/3cdqB4HhPLsd78g22mE/leGcXKiybCCQ9DpxRYNStEfGDChdes4hYCiytdT6S2iKi6PmMRuN4Ty7He/INtpjTGG/ZRBARl9WyAGA7MDNveEZStgcYJ2lIclSQKzczs37UH5ePrgDmJlcItQDXAssjIoCfAB9M6i0G+u0Iw8zMsmq9fPS3JG0DLgb+VdKjSfk0SY8AJHv7NwKPAuuBByNibTKLm4BPSNpE9pzB3bXEU6Gau5f6meM9uRzvyTfYYk5dvMrumJuZWVr5zmIzs5RzIjAzS7lUJYJij7poVJJelbRG0mpJbQMdT2+Slklql/RiXtkESY9J2pj8HT+QMeYrEu9nJW1P2ni1pF8fyBjzSZop6SeS1iWPcvnjpLwh27hEvA3ZxpKGS/ovSc8n8d6elDfko29KxPtNSa/kte/CquedlnMEyaMufg5cTvbmtRXAdRGxbkADK0HSq0BrRDTkzS2SfgU4BHwrIn4hKfsrYG9E3Jkk2/ERcdNAxplTJN7PAoci4m8GMrZCJE0FpkbEKkljgJVk776/ngZs4xLxfogGbOPk6QajIuKQpKHAfwB/DHwCeDgi7pf0j8DzEfEPAxkrlIz3Y8C/RMRDJWdQQpqOCAo+6mKAYxrUIuJpYG+v4kVkHxcCDfbYkCLxNqyI2BkRq5L3B8ledTedBm3jEvE2pMg6lAwOTV5Bgz76pkS8NUtTIij0qIuGXUkTAfxY0srkERuDwZSI2Jm8fw2YMpDBVOhGSS8kXUcN0c3Sm6QzgfOBZxkEbdwrXmjQNpbULGk10A48BrxEFY++6W+9442IXPt+PmnfuyQNq3a+aUoEg9F7I+ICsk9u/cOka2PQSG4abPS+x38AzgYWAjuBLw1oNAVIGg18D/iTiDiQP64R27hAvA3bxhHRHRELyT7Z4ELg3IGNqLTe8Ur6BeAWsnG/G5hA9v6sqqQpERR71EXDiojtyd924PtkV9RGtyvpK871GbcPcDwlRcSu5MeVAf4vDdbGSV/w94DvRMTDSXHDtnGheBu9jQEiYj/ZJx1cTPLom2RUQ24n8uK9MumSi4joAL5BH9o3TYmg4KMuBjimoiSNSk64IWkUcAXZ///Q6JaTfVwIDILHhuQ2qInfooHaODk5eDewPiL+Nm9UQ7ZxsXgbtY0lTZI0Lnk/guyFJOtp0EffFIn3Z3k7BSJ7PqPq9k3NVUMAyWVrXwaagWUR8fmBjag4SWeRPQqA7MMBv9to8Uq6D7iE7GNwdwGfAf4ZeBCYBWwGPhQRDXGCtki8l5DtsgjgVeAP8vrfB5Sk9wL/DqwBMknxp8n2uzdcG5eI9zoasI0lvYPsyeBmsjvFD0bEHclv736y3SzPAb+b7G0PqBLxPglMAgSsBj6Wd1K5snmnKRGYmdmJ0tQ1ZGZmBTgRmJmlnBOBmVnKORGYmaWcE4GZWco5EZiZpZwTgZlZyv1/L1ciFST1y3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acf = plot_acf(new_df['Second Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "53cf2ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pacf = plot_pacf(new_df[\"Second Difference\"].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "069d01eb",
   "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>Close</th>\n",
       "      <th>EWM</th>\n",
       "      <th>First Difference</th>\n",
       "      <th>Second Difference</th>\n",
       "      <th>12 Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-13 00:00:00+00:00</th>\n",
       "      <td>823.984009</td>\n",
       "      <td>877.072088</td>\n",
       "      <td>19.150024</td>\n",
       "      <td>46.226990</td>\n",
       "      <td>-174.341003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-14 00:00:00+00:00</th>\n",
       "      <td>818.411987</td>\n",
       "      <td>865.722922</td>\n",
       "      <td>-5.572021</td>\n",
       "      <td>13.578003</td>\n",
       "      <td>-203.338013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-15 00:00:00+00:00</th>\n",
       "      <td>821.797974</td>\n",
       "      <td>857.322523</td>\n",
       "      <td>3.385986</td>\n",
       "      <td>-2.186035</td>\n",
       "      <td>-222.041992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-16 00:00:00+00:00</th>\n",
       "      <td>831.533997</td>\n",
       "      <td>852.436668</td>\n",
       "      <td>9.736023</td>\n",
       "      <td>13.122009</td>\n",
       "      <td>-323.195984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-17 00:00:00+00:00</th>\n",
       "      <td>907.937988</td>\n",
       "      <td>862.872134</td>\n",
       "      <td>76.403992</td>\n",
       "      <td>86.140015</td>\n",
       "      <td>-105.442017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Close         EWM  First Difference  \\\n",
       "Date                                                                  \n",
       "2017-01-13 00:00:00+00:00  823.984009  877.072088         19.150024   \n",
       "2017-01-14 00:00:00+00:00  818.411987  865.722922         -5.572021   \n",
       "2017-01-15 00:00:00+00:00  821.797974  857.322523          3.385986   \n",
       "2017-01-16 00:00:00+00:00  831.533997  852.436668          9.736023   \n",
       "2017-01-17 00:00:00+00:00  907.937988  862.872134         76.403992   \n",
       "\n",
       "                           Second Difference  12 Difference  \n",
       "Date                                                         \n",
       "2017-01-13 00:00:00+00:00          46.226990    -174.341003  \n",
       "2017-01-14 00:00:00+00:00          13.578003    -203.338013  \n",
       "2017-01-15 00:00:00+00:00          -2.186035    -222.041992  \n",
       "2017-01-16 00:00:00+00:00          13.122009    -323.195984  \n",
       "2017-01-17 00:00:00+00:00          86.140015    -105.442017  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4776fba6",
   "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>Close</th>\n",
       "      <th>EWM</th>\n",
       "      <th>First Difference</th>\n",
       "      <th>Second Difference</th>\n",
       "      <th>12 Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-10-21 00:00:00+00:00</th>\n",
       "      <td>19172.468750</td>\n",
       "      <td>19240.626168</td>\n",
       "      <td>118.728516</td>\n",
       "      <td>32.933594</td>\n",
       "      <td>-273.957031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-22 00:00:00+00:00</th>\n",
       "      <td>19208.189453</td>\n",
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       "      <td>154.449219</td>\n",
       "      <td>66.705078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-23 00:00:00+00:00</th>\n",
       "      <td>19567.007812</td>\n",
       "      <td>19295.142988</td>\n",
       "      <td>358.818359</td>\n",
       "      <td>394.539062</td>\n",
       "      <td>515.589844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-24 00:00:00+00:00</th>\n",
       "      <td>19345.572266</td>\n",
       "      <td>19304.311948</td>\n",
       "      <td>-221.435547</td>\n",
       "      <td>137.382812</td>\n",
       "      <td>188.126953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-25 00:00:00+00:00</th>\n",
       "      <td>19484.539062</td>\n",
       "      <td>19337.080514</td>\n",
       "      <td>138.966797</td>\n",
       "      <td>-82.468750</td>\n",
       "      <td>101.634766</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Close           EWM  First Difference  \\\n",
       "Date                                                                      \n",
       "2022-10-21 00:00:00+00:00  19172.468750  19240.626168        118.728516   \n",
       "2022-10-22 00:00:00+00:00  19208.189453  19234.728583         35.720703   \n",
       "2022-10-23 00:00:00+00:00  19567.007812  19295.142988        358.818359   \n",
       "2022-10-24 00:00:00+00:00  19345.572266  19304.311948       -221.435547   \n",
       "2022-10-25 00:00:00+00:00  19484.539062  19337.080514        138.966797   \n",
       "\n",
       "                           Second Difference  12 Difference  \n",
       "Date                                                         \n",
       "2022-10-21 00:00:00+00:00          32.933594    -273.957031  \n",
       "2022-10-22 00:00:00+00:00         154.449219      66.705078  \n",
       "2022-10-23 00:00:00+00:00         394.539062     515.589844  \n",
       "2022-10-24 00:00:00+00:00         137.382812     188.126953  \n",
       "2022-10-25 00:00:00+00:00         -82.468750     101.634766  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "27fc808c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler \n",
    "scaled = MinMaxScaler()\n",
    "values = scaled.fit_transform(np.array(new_df['Close']).reshape(-1,1))\n",
    "new_df['Close'] = values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "72a61767",
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime,timedelta\n",
    "train_dataset_end=datetime(2022,9,19)\n",
    "test_dataset_end=datetime(2022,10,19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b9ac220e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2112, 5)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c4f64a6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=new_df[:train_dataset_end]\n",
    "test_data=new_df[train_dataset_end+timedelta(days=1):test_dataset_end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "21740d40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2076, 5)\n",
      "(30, 5)\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "70631040",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tushar nautiyal\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\tushar nautiyal\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\tushar nautiyal\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "model_ARIMA=ARIMA(train_data['Close'],order=(1,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7260232e",
   "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>2076</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th>  <td>5871.044</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Tue, 25 Oct 2022</td> <th>  AIC                </th> <td>-11736.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:51:09</td>     <th>  BIC                </th> <td>-11719.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>01-13-2017</td>    <th>  HQIC               </th> <td>-11729.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 09-19-2022</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>   -0.0893</td> <td>    0.493</td> <td>   -0.181</td> <td> 0.856</td> <td>   -1.056</td> <td>    0.877</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>    0.0630</td> <td>    0.495</td> <td>    0.127</td> <td> 0.899</td> <td>   -0.907</td> <td>    1.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0002</td> <td> 2.51e-06</td> <td>   81.145</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</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>10498.32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>1.00</td>  <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>15.06</td> <th>  Skew:              </th>   <td>-0.21</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>   <td>14.01</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[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:                 2076\n",
       "Model:                 ARIMA(1, 1, 1)   Log Likelihood                5871.044\n",
       "Date:                Tue, 25 Oct 2022   AIC                         -11736.089\n",
       "Time:                        19:51:09   BIC                         -11719.175\n",
       "Sample:                    01-13-2017   HQIC                        -11729.890\n",
       "                         - 09-19-2022                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.0893      0.493     -0.181      0.856      -1.056       0.877\n",
       "ma.L1          0.0630      0.495      0.127      0.899      -0.907       1.033\n",
       "sigma2         0.0002   2.51e-06     81.145      0.000       0.000       0.000\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             10498.32\n",
       "Prob(Q):                              1.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              15.06   Skew:                            -0.21\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        14.01\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_Arima_fit=model_ARIMA.fit()\n",
    "model_Arima_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8c64652b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acf = plot_acf(new_df['12 Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "db59c67f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pacf = plot_pacf(new_df['12 Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "98c8a5d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tushar nautiyal\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "C:\\Users\\tushar nautiyal\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    }
   ],
   "source": [
    "# P D Q Values in Sarimax \n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "model_SARIMA=SARIMAX(train_data['Close'],order=(6,0,11),seasonal_order=(0,1,0,12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fcb77cef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tushar nautiyal\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "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>2076</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(6, 0, 11)x(0, 1, [], 12)</td> <th>  Log Likelihood     </th>  <td>5711.686</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Tue, 25 Oct 2022</td>         <th>  AIC                </th> <td>-11387.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>19:51:16</td>             <th>  BIC                </th> <td>-11285.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                     <td>01-13-2017</td>            <th>  HQIC               </th> <td>-11350.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>- 09-19-2022</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>   -0.0797</td> <td>    0.018</td> <td>   -4.385</td> <td> 0.000</td> <td>   -0.115</td> <td>   -0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.0257</td> <td>    0.015</td> <td>    1.681</td> <td> 0.093</td> <td>   -0.004</td> <td>    0.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>    0.0211</td> <td>    0.017</td> <td>    1.224</td> <td> 0.221</td> <td>   -0.013</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4</th>  <td>   -0.0177</td> <td>    0.016</td> <td>   -1.090</td> <td> 0.276</td> <td>   -0.049</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5</th>  <td>    0.1124</td> <td>    0.017</td> <td>    6.703</td> <td> 0.000</td> <td>    0.080</td> <td>    0.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L6</th>  <td>    0.0696</td> <td>    0.015</td> <td>    4.666</td> <td> 0.000</td> <td>    0.040</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>    0.9729</td> <td>    0.013</td> <td>   72.399</td> <td> 0.000</td> <td>    0.947</td> <td>    0.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>    0.9369</td> <td>    0.017</td> <td>   56.004</td> <td> 0.000</td> <td>    0.904</td> <td>    0.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>  <td>    0.9416</td> <td>    0.018</td> <td>   52.097</td> <td> 0.000</td> <td>    0.906</td> <td>    0.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L4</th>  <td>    0.8934</td> <td>    0.020</td> <td>   44.056</td> <td> 0.000</td> <td>    0.854</td> <td>    0.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L5</th>  <td>    0.8589</td> <td>    0.016</td> <td>   53.309</td> <td> 0.000</td> <td>    0.827</td> <td>    0.890</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L6</th>  <td>    0.9146</td> <td>    0.013</td> <td>   68.592</td> <td> 0.000</td> <td>    0.888</td> <td>    0.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L7</th>  <td>    0.9197</td> <td>    0.014</td> <td>   64.677</td> <td> 0.000</td> <td>    0.892</td> <td>    0.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L8</th>  <td>    0.8797</td> <td>    0.017</td> <td>   52.335</td> <td> 0.000</td> <td>    0.847</td> <td>    0.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L9</th>  <td>    0.8995</td> <td>    0.016</td> <td>   55.591</td> <td> 0.000</td> <td>    0.868</td> <td>    0.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L10</th> <td>    0.8905</td> <td>    0.015</td> <td>   60.752</td> <td> 0.000</td> <td>    0.862</td> <td>    0.919</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L11</th> <td>    0.8365</td> <td>    0.010</td> <td>   80.721</td> <td> 0.000</td> <td>    0.816</td> <td>    0.857</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0002</td> <td>  3.3e-06</td> <td>   68.305</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>8.81</td>  <th>  Jarque-Bera (JB):  </th> <td>9094.42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.00</td>  <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>15.03</td> <th>  Skew:              </th>  <td>-0.29</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>  <td>13.27</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[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:                 2076\n",
       "Model:             SARIMAX(6, 0, 11)x(0, 1, [], 12)   Log Likelihood                5711.686\n",
       "Date:                              Tue, 25 Oct 2022   AIC                         -11387.372\n",
       "Time:                                      19:51:16   BIC                         -11285.989\n",
       "Sample:                                  01-13-2017   HQIC                        -11350.205\n",
       "                                       - 09-19-2022                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.0797      0.018     -4.385      0.000      -0.115      -0.044\n",
       "ar.L2          0.0257      0.015      1.681      0.093      -0.004       0.056\n",
       "ar.L3          0.0211      0.017      1.224      0.221      -0.013       0.055\n",
       "ar.L4         -0.0177      0.016     -1.090      0.276      -0.049       0.014\n",
       "ar.L5          0.1124      0.017      6.703      0.000       0.080       0.145\n",
       "ar.L6          0.0696      0.015      4.666      0.000       0.040       0.099\n",
       "ma.L1          0.9729      0.013     72.399      0.000       0.947       0.999\n",
       "ma.L2          0.9369      0.017     56.004      0.000       0.904       0.970\n",
       "ma.L3          0.9416      0.018     52.097      0.000       0.906       0.977\n",
       "ma.L4          0.8934      0.020     44.056      0.000       0.854       0.933\n",
       "ma.L5          0.8589      0.016     53.309      0.000       0.827       0.890\n",
       "ma.L6          0.9146      0.013     68.592      0.000       0.888       0.941\n",
       "ma.L7          0.9197      0.014     64.677      0.000       0.892       0.948\n",
       "ma.L8          0.8797      0.017     52.335      0.000       0.847       0.913\n",
       "ma.L9          0.8995      0.016     55.591      0.000       0.868       0.931\n",
       "ma.L10         0.8905      0.015     60.752      0.000       0.862       0.919\n",
       "ma.L11         0.8365      0.010     80.721      0.000       0.816       0.857\n",
       "sigma2         0.0002    3.3e-06     68.305      0.000       0.000       0.000\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   8.81   Jarque-Bera (JB):              9094.42\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              15.03   Skew:                            -0.29\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        13.27\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_SARIMA_fit=model_SARIMA.fit()\n",
    "model_SARIMA_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "60a31491",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>EWM</th>\n",
       "      <th>First Difference</th>\n",
       "      <th>Second Difference</th>\n",
       "      <th>12 Difference</th>\n",
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       "      <th>2022-10-15 00:00:00+00:00</th>\n",
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       "      <th>2022-10-17 00:00:00+00:00</th>\n",
       "      <td>0.280641</td>\n",
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       "      <td>282.664062</td>\n",
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       "      <th>2022-10-18 00:00:00+00:00</th>\n",
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       "      <td>19336.475624</td>\n",
       "      <td>-216.341797</td>\n",
       "      <td>66.322266</td>\n",
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       "    <tr>\n",
       "      <th>2022-10-19 00:00:00+00:00</th>\n",
       "      <td>0.274480</td>\n",
       "      <td>19300.668266</td>\n",
       "      <td>-194.880859</td>\n",
       "      <td>-411.222656</td>\n",
       "      <td>-407.314453</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Close           EWM  First Difference  \\\n",
       "Date                                                                  \n",
       "2022-10-15 00:00:00+00:00  0.273403  19294.155206       -118.021484   \n",
       "2022-10-16 00:00:00+00:00  0.276406  19289.416759        200.458984   \n",
       "2022-10-17 00:00:00+00:00  0.280641  19336.933314        282.664062   \n",
       "2022-10-18 00:00:00+00:00  0.277400  19336.475624       -216.341797   \n",
       "2022-10-19 00:00:00+00:00  0.274480  19300.668266       -194.880859   \n",
       "\n",
       "                           Second Difference  12 Difference  \n",
       "Date                                                         \n",
       "2022-10-15 00:00:00+00:00        -315.269531    -555.945312  \n",
       "2022-10-16 00:00:00+00:00          82.437500   -1068.750000  \n",
       "2022-10-17 00:00:00+00:00         483.123047    -609.958984  \n",
       "2022-10-18 00:00:00+00:00          66.322266    -621.027344  \n",
       "2022-10-19 00:00:00+00:00        -411.222656    -407.314453  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "98861e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>EWM</th>\n",
       "      <th>First Difference</th>\n",
       "      <th>Second Difference</th>\n",
       "      <th>12 Difference</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th>2022-09-20 00:00:00+00:00</th>\n",
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       "      <td>0.278585</td>\n",
       "      <td>19572.638224</td>\n",
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       "      <th>2022-09-23 00:00:00+00:00</th>\n",
       "      <td>0.276849</td>\n",
       "      <td>19522.638306</td>\n",
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       "      <td>0.271446</td>\n",
       "      <td>19416.160744</td>\n",
       "      <td>-360.626953</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Close           EWM  First Difference  \\\n",
       "Date                                                                  \n",
       "2022-09-20 00:00:00+00:00  0.270754  19843.677789       -653.339844   \n",
       "2022-09-21 00:00:00+00:00  0.265609  19607.990989       -343.388672   \n",
       "2022-09-22 00:00:00+00:00  0.278585  19572.638224        866.150391   \n",
       "2022-09-23 00:00:00+00:00  0.276849  19522.638306       -115.912109   \n",
       "2022-09-24 00:00:00+00:00  0.271446  19416.160744       -360.626953   \n",
       "\n",
       "                           Second Difference  12 Difference  \n",
       "Date                                                         \n",
       "2022-09-20 00:00:00+00:00        -528.716797    -439.044922  \n",
       "2022-09-21 00:00:00+00:00        -996.728516   -2833.751953  \n",
       "2022-09-22 00:00:00+00:00         522.761719   -2266.988281  \n",
       "2022-09-23 00:00:00+00:00         750.238281   -2471.617188  \n",
       "2022-09-24 00:00:00+00:00        -476.539062   -3433.437500  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c2d4e8fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-09-20 00:00:00+00:00\n",
      "2022-10-19 00:00:00+00:00\n"
     ]
    }
   ],
   "source": [
    "##prediction\n",
    "pred_start_date=test_data.index[0]\n",
    "pred_end_date=test_data.index[-1]\n",
    "print(pred_start_date)\n",
    "print(pred_end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0b5cb961",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_Sarima = model_SARIMA_fit.predict(start=pred_start_date,end=pred_end_date)\n",
    "residuals=test_data['Close']-pred_Sarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "418c44dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_SARIMA_fit.resid.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "41b0ac3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_SARIMA_fit.resid.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fcd0d673",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Predicted_SARIMA']=pred_Sarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "934f9353",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_arima = model_Arima_fit.predict(start=pred_start_date,end=pred_end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "945f1f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['Predicted_ARIMA'] = pred_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "395fae27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-01-13 00:00:00+00:00\n",
      "2022-09-19 00:00:00+00:00\n"
     ]
    }
   ],
   "source": [
    "pred_start_date=train_data.index[0]\n",
    "pred_end_date=train_data.index[-1]\n",
    "print(pred_start_date)\n",
    "print(pred_end_date)\n",
    "pred_arima = model_Arima_fit.predict(start=pred_start_date,end=pred_end_date)\n",
    "pred_Sarima = model_SARIMA_fit.predict(start=pred_start_date,end=pred_end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "70b318dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['Predicted_ARIMA'] = pred_arima\n",
    "train_data['Predicted_SARIMA']= pred_Sarima\n",
    "train_data[['Close','Predicted_ARIMA','Predicted_SARIMA']].plot(figsize = (10,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "78221a97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EZEbwwJYHWkRK5a/DvqaDQweu8rvKoOWG+IRQUFbA4eTDBi23OWQWlJJbXN5sASCrOIsFWxYgEHw66VM6OXXik0mf4GLlQY7L5xw8H9Es9ahLaGooR1OPMq/fPKwtrA1WrqutK0M6DFHjAHr6JeoXvjz5Jdf1uI57B9zLxK4TeXb4s+xK2MXLB15u9H4S+tz6DQNipJSxUspS4GfgmuonSCm3Sykr2yIHgMolghKwA2wAW8AaML/E5w2QlJ/E/3b8j64uXXl9zOtVd9cTfCbwzvh3CEsPY8GWBU1uujXF8bTjHEo+xO19b8fa0nBvaoDhnYZjb2XPtvPbDFpuc4jPaL4kcMXlxTy07SFSClP4aOJHdHXpCoCbnRsvDf8AiQWP715IWqFpcwMtD1tOO9t2zO4+2+Blh/iEEJMdw/nctrfjXEPsTdzLywdeZnTn0Tw74tmq8bobe93I/MD5/Br9K58d/6xRZesTALoA1f9CCbpjtbkb2AAgpdwPbAeSdF//SCnDq527Qtf985yoZRRSCDFfCHFYCHE4La1lJ8oqLi9m0fZFlGnK+DDkw8syak7qNok3x73J8bTjPLj1QZMFga9Pfo2LjQvX97ze4GXbWdkxstNIdpzfYXa7XMU2UxZQjdTwzJ5nOJF2gtfHvk6QV9BFj0/w74N12j3klWWzYOsCk+XUj8mKYcf5Hdzc52YcrA0/K6pyQHlHwg6Dl91aRGRG8NiOx+jerjvvTnj3slbYwqCFzOo+i8+Of8baqLUNLt+gg8BCiFuBYOBt3c/dgT5oWwRdgIlCiLG602+RUg4Axuq+bqupTCnlF1LKYCllsKenpyGra1BSSl7c/yIRmRG8MfYN/Fz9ajzvCt8reG3MaxxNPcrD2x6muLy4WesZmx3LtvPbjPamBu1soJTCFE5nnjZK+cYSl16AlYXAu729UZ/nvcPvsfnsZh4Pfpwp3aZc9riFhWCkdxC2mXcSkxXDozsepayizKh1qsmKUyuwt7Jnbq+5Rinfx9mH7u26q26gWiQXJPPglgdxtnFm6aSlOFpffmMihGDJyCWM6TKGlw+83ODfpT4BIBGoniDGW3fs0opMBp4FZkopKycxzwYOSCnzpZT5aFsGIwGklIm6/+cBP6HtajJb35/+nr9i/+LBoAfrnSo33X86r4x+hX+T/2XR9kXNmkVzxakV2FnacXPv2lM+N9V47/FYCAuze2PHZxTQ1c0BK0vjTY77Kfwnvj39LTf3vpnb+tZ4zwNo8wKlpPjyUOAzHEg6wHP7nmvWKZMX8i+wPnY91/W4jnZ27Yz2PCE+IRxJOdKiZsi1BHmleSzYuoDC8kI+nfwpHRw71HqutYU1745/lz5ufVi8czHH047r/Tz6/Es/BPQQQvgJIWyAOcC66icIIQYBn6P98K8+mHsOGC+EsBJCWKMdAA7X/eyhu9YauBow2/lgB5IO8N6R95jcdTL3Bt6r1zUzAmbw4qgX2XdhH49uf7RZZs0kFyTzV+xfXNvjWtrbtTfa87S3a0+QZ5DZrQqOTSsw6h4A289t581DbxLiE8ITQ5+oc+3FqAB3ABxKh/PwoIf5O/ZvPjz6odHqdqnvTn8H0KDssI0xwWeCNjlcokoOV6lMU8ZjOx4jLjuO9ya8R8/2Peu9xsHagaWTluLp4MnCrQuJz4nX67nqDQBSynJgIfAPEA6sllKeEkK8JISYqTvtbcAJWKPr068MEGuBM8BJ4DhwXEr5J9oB4X+EECeAULQtii/1qnELk5ifyOKdi/F18eWVMa80aErl7B6zeX7k8+xO3M3/dvzP6M38705/h5TS6G9q0N7ZmVNyOI1Gcjaj0Gj9/2HpYTyx6wn6uvXlzXFvYmlR90KzAE8nPJ1t2R+bwT0D7uGmXjfxddjX/Bj+o1HqV11WcRa/RP3CVf5X0dGxo1Gfq79HfzzsPcyutWgsUkpe3PciB5IO8Pyo5xnZeaTe17rbu7Ns8jIshAX3b7lfr5Tben1aSSnXSyl7SikDpJSv6o4tkVKu030/WUrZodqUzpm64xVSyvuklH2klH2llI/pjhdIKYdIKQOllP2klIuklIZZ29yMisqLWLRtERWygo8mflRjH119ru95Pf83/P/YkbCDxbsWU6YxfBDQSA1/xf7F2qi1TPebTmenzgZ/jkuFdNUuLjOXN3ZKXjFFZRVGaQEk5CXw4NYHcbd35+NJH2NvVf8YgxCCEf7uVXmBnh72NBN9JvLmv2+y+exmg9exUmxOLE/tforiiuImZYfVl4WwYLz3ePYk7jHJOEdLs+zEMv448wcPDHyAWd1nNfj6ri5dWTppKZnFmSzYsqDeKedqJXAjSSl5fu/zRGVF8da4t6qm8TXGTb1v4qlhT7H13Fae2vWUQbMkhqaGcuv6W3l699P4ufqxcNBCg5VdF3NLDleVBM7AASCnJIcFWxdQrinns8mf1bvbWnWjAtxJyyvhTFoBlhaWvDnuTQZ6DuSpXU9xJOWIQeuZlJ/Ekr1LmP3HbEJTQ3li6BP4t/M36HPUpnLtyKGUlrP4zRT+iPmDT0M/ZWbATB4Y+ECjy+nv0Z93x79LVFYUj26vewKBCgCN9M2pb9gQv4GHBz/MmC5jmlzeLX1uYXHwYjad3cTkNZN57/B7XMi/0OjyLuRf4ImdT3DbhttIKUjhldGvsPKqlc1y91/JnJLDVQYAQ7YASitKWbR9EQl5CXw08aNaZ4bVZqS/dhygMi+QnZUdH0/8mM5OnXlo20PEZMU0uY5ZxVm8fehtrv7tav6K/Yube9/Mhus21DlAbWjDOw3HztLObG4WjOFA0gFe2PcCwzsO54WRLzQ5N9dY77G8MOoF9iftZ8m+JbWepwJAI+xL3McHRz9garepTU6jXN3t/W7nu2nfMaLTCL47/R3Tfp3Go9sf5XDyYb3n1BeUFfDR0Y+Y+ftMtp/fzv0D7+fP2X9yTfdrDJbyQV+VA3zmkBwuPr0AWysLOrnYGaQ8jdTwf3v/jyMpR3h1zKuNSqHczd2Bzq527D/zX19uO7t2LJuyDFtLW67/83ru2HAHX538isjMyAatuygoK+Cz458x7ddp/BD+A9P9p/PX7L94ctiTuNm5NbiuTWFnZcfIziPZkWB+a0cMITormke3P4qvqy/vh7xvsMWZs7rP4qFBD/FX7F+1ntN8u163Emeyz7B412IC2gXw8uiXDZJFs7pBXoMY5DWIpPwkVkWuYm30Wrac20Kv9r24pc8tTPefjq2l7WXXVWgqWHdmHR8d+4j0onSu8r+KRwY/YvRBvLoEegZWJYe7yt+wKScMLS69AF93RywsDPP3/OTYJ2yI28CiwYsanUNHCMGIAHd2RKah0ciqunVx6sL3077nt5jf2J2wmw+PfsiHRz/Ey8GLsV3GMrbLWEZ0HlHjmFRpRSlrotbwxYkvyCzOZFLXSTw06CEC2gU06fU2VYhPCNvPbycyK7JJGxOZm9TCVBZsXYC9lT2fTvoUZxtng5Z/74B7cbVxZQ5zanxcmFPEDQ4OlocPmy7HzIm0EyzYugArYcX3077Hx6Vh++c2RnF5MX/H/s0P4T8Qkx1De9v2XN/zem7qdVPV3OBDyYd469BbRGRGEOgZyJNDnyTQM9DoddPHC/teYGP8RnbdtAsbSxtTV6dWk97dQQ8vZ5bd1rTNTgDWRq3lxf0vcn3P61kyYkmTbhLWHkng8TXH2bBoLH06udR4TlphGnsS97A7cTf7L+wnvywfKwsrhngNYUyXMYz1Houviy9/x/3N0mNLuVBwgWEdh7Fo8KIW8+8koyiDkNUhPBD0QJP6v81JYVkh8zbOIz43nm+v/JY+7n2M9lxCiCNSyss2/VABQE/7EvfxyI5HcLdz54upXzR48/SmklJyKPkQP4b/yPbz27EUlkzuNpkyTRlbz22lo2NHHhvyGFf6XmnwVklT7Di/g4e2PcTnkz9nVJdRpq5OjcorNPRZspG7x/jz1LSm3X3uTdzLg1sfZETnEXwy8ROsLJrWyE7MLmL0G9tYcnVf7hpT/xhCmaaM46nH2Z24uyqNM4CDlQOF5YX0de/LosGLGNlpZIv6dwJw2/rbKNWUsurqVaauitGVa8p5eNvD7Luwj48mfsQ473FGfb7aAoDqAtLDxviNPL37aQJcA1g2ZVmDZnIYihCCYZ2GMazTMBLyEvg54md+jf6VclnOQ4Me4va+t2NnZZj+a0Ma0WlEVXK4lhoALmQXU1YhmzwDKDIzkv/t/J82b8v4d5v84Q/QpZ093dwd2HcmQ68AYG1hTXDHYII7BvPokEdJLkhmT+IeQlNDGec9jindprS4D/5KE3wm8MHRD0guSDZp16WxSSl5/eDr7E7czXMjnjP6h39d1CBwPVZFrOKJnU8Q6BHI11d+bZIP/0t5O3vz+NDH2XbjNnbcuIP5gfNb5Ic/mEdyuNh0bbK1pswASi5IZsHWBThZO9Wat6WxRvq7czAugwpNw39/HR07cn3P63llzCtM9Z3aYj/8gaqNiXae32nimhjXilMrWB21mrv638WNvW40aV1UAKiFlJJlx5fxysFXGOc9jmVTluFiU3MfrKnYWdkZLaGbIbX05HDx6U3LAppfms+DWx+koKyApZOW1pm3pTFGBriTV1zOqQstfzptU/i5+tHNpRvbE1rvdNCNcRt5/8j7TPOdxqLBi0xdHRUAaqKRGt489CZLQ5cyM2Am74e8r9fqTaVm47zHtejkcHHpBTjZWuHh1PBB6jJNGY/vfJwz2Wd4b/x79HLrZfD6Va0HaEHbRBqDEIIJ3hP4N+nfFrFpkqEdTTnKM3ueYbDXYF4e83KzT8uuielr0MKUacp4Zs8z/Bj+I7f1vY2XR79s0J2Q2iI3O7cWnRwuTpcDqKHdI1JKXj3wKnsv7GXJyCVGG+PwcrGju5dTVVqI1myCzwTKNGXsu7DP1FUxqPiceB7e/jBdnLrwYciHNU7lNgUVAKqpzO3zd+zfLBq8iMXBi1tElG4NWnJyuLj0/Eb1/y8PW84v0b9w74B7ubbHtUao2X9G+rtzKD6TsormSwltCkFeQbjaurbYm4XGyCzO5IEtD2ApLPl08qdGTa/dUOrTTSenJIf5m+azJ3EPS0Yu4Z4B97ToATNz01KTw5WUV5CYVdTg/v/1sev58OiHTPebzkODHjJS7f4zKsCdwtIKTiRkG/25TMnKwopxXcaxK3FXkzY7bykqt/5MK0rjo4kfNfv08fqoAIB2Nd68jfM4lXGKd8a/ww09bzB1lVqdbi7d8HP1Y/v5lnVndz6zEI0EPw/9B9MPJx/m//b+H0M6DDHKavCaDG8j4wCgvVnIKckhNDXU1FVpkgpNBU/vfpqTaSd5c6w2kV9L0+YDQElFCfM3zedC/gU+nfwpU32nmrpKrVaITwhHko+QW5pr6qpUiUvX7svs5+FUz5m683PiWLR9UVVfbnOtbnZztKFPJ5c2MQ4wqvMorC2sW1xrsaHePfIuW85tYfHQxUzqNsnU1alRmw8AX5z4gjM5Z3h3wruM6DTC1NVp1UJ8QiiX5exOaDm7P8Xp1gD4udffBZRelM6CLQuwsrDi08mf4mrrauzqXWSkvztHzmZRXGZ2W2c0iKO1I8M6DWP7+e0tdu1IfX4M/5HvT3/PLX1uadbMqg3VpgNAZGYkX5/8mpkBMw2S0lmpW6BnIG52bi2qGyguvRA3RxtcHeqe6ZVdnM38zfPJKM7g44kfm6Qvd2SAOyXlGo6dy272525uId4hnMs7R1xunKmr0mC/x/zOG/++wUSfiSwOXmzq6tSpzQaAck05S/YtwcXWhSeGPmHq6rQJFsKCCT4TWtTuT3Hp+fi6193/n1uay/zN8zmbc5aPJn5ksgRqw/zcsBD/7Q/Qmo33GQ9gdrOBNsRt4Pl9zzOq8yjeGv9WvVt/mlqbDQA/nP6B0xmneWb4M83elG/LqnZ/Sm4Zuz+dSSvA37P2/v/CskIWbFlAdHY074e8b9JuQld7a/p3cb1of4DWqqNjR/q69zWrcYBt57bx9O6nGeQ1iA9CPmgxc/3r0iYDwLncc3wS+gkTfSYytZsa9G1O1ZPDmVp6fglpeSX07lhzDvbi8mIWbltIWHoYb417y6RJuyqNDHAn9Hw2haXmP0WyPhN8JnA87TgZRS2/xbM3cS+P73ycfu79WDppqdlkDmhzAUBKyQv7X8DGwoZnRzyr5vo3s8rkcNvPbzf5PO+IpDyAGvPsl1aU8siORzicfJhXx7zKlG5Tmrt6NRrp705ZheRwfJapq2J0IT4hSCQ7E1p2crhDyYdYtH0RAe0C+GzKZwZNBGhsbS4A/BL9C4eSD/G/4P/h5eBl6uq0Sdd0v4bUwlR+OP2DSesRkaydjnppC6BMU8binYvZm7iXF0e92KJ2Mxvq64aVhWgT4wC92vfCz9WPL098SVF5kamrU6PQ1FAe3Pog3k7efD7l8xaXMLI+bSoApBSk8O7hdxnWcZjRl+4rtQvxCWGCzwSWhi7lfN55k9UjPCkPL2db3J3+66ut0FTw7O5n2XZ+G08Pe5rZPWabrH41cbS1YqBPuzaxHkAIwXMjniMhP4FPQz81dXUuczrjNAu2LMDT3pMvp37Z7HspG0KbCQBSSl45+ArlmnJeGPmC6voxoKyCUq79dC+rDp3T63whBM8OfxZLC0te2v+SyeZ6RyTn0rta949Ganh+3/NsiN/AY0Me4+Y+N5ukXvUZFeDOyYRscotbxkwqYxracSjX9biO705/x6n0U6auTpWYrBju23wfzjbOfDX1KzwdPE1dpUZpMwHgn7P/sOP8DhYOWtgse/m2FVJKnvzlBEfPZfPc76cIS9QvZ31Hx448OvhRDiQdYN2ZdUau5eXKKzREp+TTR9f9I6XktYOv8ceZP1gwcAF39r+z2eukr5H+7mgkHIrLNHVVmsVjwY/hYefBkn1LKNOYPujF58Rzz6Z7sLGw4aupX9HJqZOpq9RobSIAZBdn8/rB1+nv3p9b+txi6uq0Kiv/Pc+m0yksDOmOm6MNC386Sn6JfoO7N/S6gUFeg3jr0FukFzXv1MbY9AJKKzT07uSMlJJ3Dr/DqshV3NX/Lu4feH+z1qWhBndrj42VRZvICwTgYuPCsyOeJSorihVhK0xal8T8RO7ZdA8SyZdTvzTIzWR5hYZfjiSYZIW3ee0JXJoPcbsBCVLW8n/Q/afK29E/kVuSzRe97sLqTG0LS1SXUENdyCli219hLPB25jE/G2Y45vP6+nC+/z6S+8cHIOr5nVoAL3S+guvT3uatbY/xVs/bm6fiQEZsOuMtYhlSWs7Sbe/zXcImbu44lkec+yNitjZbPRrDDrjLK4a0k6co8j+PvXXLXmxkCBOBqe4DWRb6KZOlI/4Oht11TR8pJdncHfYxRRXFfN1vAf5psZAW2+RyD8Wms25nLDbx3swI7GyAmupPmFOujeDOlvLwfP2SdlXaY2/HAx29uC8rh4XZrXtLPXO1rJ0LS9u345PkVMYXFTfrc3/j4sy77u25Li+fJemZbaNJbKbSLSy4xrsT3cvKWJGU2qx/qxwLC27t1IE0K0u+Skqlf2lpMz5704kXc49IKYMvO65PABBCXAl8CFgCX0kp37jk8ceAe4ByIA24S0p5VvfYW8BVaG/4NgOLpJRSCDEE+AawB9ZXHq+rHsEDesnD674EBAhR7/8LyouYffA57C1sWTP8BWxq29nLjIJgS/Ht/nh+P5bI09P7MMz3v9kPFVLy4p+niEzO4+3rA+nqVv+c6DJNOTceepG88kL+GP4Kjs2wiOblv0+RXJDLQa9vGO3Wn/cGPIilmW3+88fxRL7ZG8/cYV25MbhtjGv9nrSH58K/5tmetzLHe2KzPe9ncev4NO53Vgx6kuD2htv2M7OghHu+O0ygdzuOn89m3mhfrhnYxWDlVxJdh9UYAJBS1vmF9kP/DOAP2ADHgb6XnBMCOOi+fwBYpft+FLBXV4YlsB+YoHvsX2AE2r6XDcC0+uoyZMgQ2RCvHXhNDvhmgDyWcqxB1yl12xOdJrs9+Zd85tcTNT6eklMkh7y8SU5+d4csLCnXq8xjKcfkgG8GyFcPvGrIqtZqxGtb5LU/vCH7f9NfRmRENMtzGppGo5GP/HxM+j71l9wanmzq6jQLjUYj7/3nXjnsh2EyKT+pWZ6zrKJMTlo9Sc7fNN/gZS/bESO7PfmXPJOaJ+d8vl8Oe3WzLCrV7z3TEMBhWcNnqj63PMOAGCllrJSyFPgZuOaSILJdSlmo+/EA4F35ENouSxvAFrAGUoQQnQAXKeUBXeW+A2bpURe9HUs9xsqIldzc52aCvIIMWXSbllVQymOrQwnwdOT/rupb4zleLna8d2MQ0an5vPinflP3gryCmNt7Lj9H/Gz0jUCyC0tJyikiTexgoOdAo2zk3hyEELx+7QD6dXZh0cpQzqTlm7pKRieEYMnIJUgkLx94uVmmEO9O2E1KYQo39rrRoOVKKfnlaAKDurbD39OJhRO7k5JbwtojCQZ9nrroEwC6ANVX6yTojtXmbrR39Egp9wPbgSTd1z9SynDd9dVfZa1lCiHmCyEOCyEOp6Wl6VFd7SYvz+97nk6OnXh40MN6XaPUT+qmfGYWlPLhnEHY29Q++DiupycLJgTw86Hz/BGq3z7ADw9+mA6OHXhh3wtGzRYanpSHpUMsWWUJBn9TNzc7a0s+vy0YaysL5n93mLw2sDbA29mbhUEL2ZWwi/Vx643+fKuiVuFl78V47/EGLTcsMZeolHyuG6y9Xx4V4M6gru34bMeZZtv72aCdnkKIW4Fg4G3dz92BPmhbBF2AiUKIsQ0pU0r5hZQyWEoZ7Omp32KL709/T1xOHEtGLsHBWv+t/pS6VU75fOKK3vTvUn8G1cem9GRIt/Y88+tJ4tIL6j3f0dqR50Y8x5mcM3wV9pUhqlyjiORcrNsdxNnapVUkA+zSzp6lNw8mPqOQx1YfR6Np/WNat/S5hQEeA3jz3zfJKjZeXqTzeefZl7iP63peh5WFYSdN/nI0ARtLi6qZP0IIHprYncTsIv4IvWDQ56qNPgEgEag+wuStO3YRIcRk4FlgppSyRHd4NnBASpkvpcxH2zIYqbveu9rlNZbZGDklOXwd9jUTvCcwustoQxSpADGp+bz01ynG9vDg7jF+el1jZWnBR3MHYWVpwUMrj1JSXv8853He45jmN027U1v2maZWu0bHLyRg7XKKWd2vwc7KzijP0dxGBrjzf1f1YfPpFD7aFm3q6hidpYUlL4x6gbzSPN489KbRnueXqF+wEBYGTx1TWq5h3fELTO7rddFmRCG9vOjbyYVPt8dQ0QyBXJ8AcAjoIYTwE0LYAHOAi5ZuCiEGAZ+j/fBPrfbQOWC8EMJKCGENjAfCpZRJQK4QYoTQ5mS4HfjDAK+Hr8O+Jr80n4cGP2SI4hS0/1gfWXUMe2tL3rlhIBYW+q+Z6NLOnnduGEhYYi6vr4/Q65onhz6Jo7UjL+x7AY00fFP4WNYmEBXc0OsGg5dtSvNG+XLt4C58sCWazadTTF0do+vZvid3D7ibv2P/Nso2o2UVZfwW8xvjvcfT0bGjQcveGZVGZkFpVfdPJSEECyd2Jza9gPUnkwz6nDWpNwBIKcuBhcA/QDiwWkp5SgjxkhBipu60twEnYI0QIlQIURkg1qKdQXQS7eyh41LKP3WPLQC+AmJ052xo6otJLUzlp/CfuMr/Knq279nU4hSddzdFEpaYy1vXD6SDS8PvmKf07cC8Ub58sy+ef04l13u+u707Twx9gtC0UFZHrm5MlWtVVlFBhsVOvKz64eeqX0vGXAgheG32AAK9XXl0VSgxqa1/UHh+4Hz8Xf156cBLFJTV383YEFvObSGzONMo40S/HEnAw8mGcT0v79a+sl9HAjwdWbo9xujdeXqNAUgp10spe0opA6SUr+qOLZFSrtN9P1lK2UFKGaT7mqk7XiGlvE9K2UdK2VdK+Vi1Mg9LKfvrylwoDTCc//nxzymX5SwIWtDUohSdvTHpfL4rlluGd2VK38avvnx6em/6d3HhibUnSMyuP7XvDP8ZjOw0kg+OfkByQf1BQ19/RG5DWGcxtuMMg5XZkthZW7Ls1iHYWWsHhVt7wjgbSxteHPUiKQUpfHj0Q4OWvTpyNd5O3ozsPNKg5WYVlLI1IoWZA7tgbXn5R7CFheDBkO5EJOexNSK1hhIMx7xWvtThXO45fo3+let7XG+SDbtbI32mfOrL1sqST+YOpkIjeXjlsXpnOVRO99NIDa8eeNVg0/1WR61BU+7ErJ5XGKS8lqizblD4XGYhj/4cqtddZIVGcuxcFu9vjuKaT/Zwy1cHTJaltaGqTyE+lnrMIGXGZsdyOOUwN/S6AQsDLxD868QFyiok1w2pfTLlzIGd8XGz55Nt0Ub9O7SaAPBJ6CdYW1pz38D7TF2VVqEhUz715evhyGvXDuDI2Sze2xxV7/nezt48GPQgOxJ28M/Zf5r8/MkFyUTkHKQiZyi9O7Zvcnkt2XB/d5bM6MvWiFQ+2FrzoHBGfgm/HUtg0c/HCH5lM7M/3cdH26LJKixjb0wGJxLMJ3XKosGL6OjYkef3PU9JRUn9F9RjddRqrCysmNV9VtMrd4m1RxPp3dGZfp1rn0lnZWnBA+O7czwhh93RxkuU2CoCQERmBBviNnBrn1vxsPcwdXVahV+PJjZoyqe+Zg7szNxhPny244xe6wNu6XML/dz78frB18ksblr641+if0Ei6WQxAbs2kEDtthHduGGINx9tjWZjWPLFd/lL9xL86hYeXXWcPdHpTOjlxYdzgjjyf1P486ExWFsK/jzePFMRDcHB2oElI5cQlxPHsuPLmlRWUXkR62LWMaXbFINv8hKTms/x89mXDf7W5LohXejoYscn22MMWofqzCsbaC0+PPohLjYuzOs/r1HXn80owMHGCk9n2/pPbiO+P3CW3h2d9Z7y2RAvzOzHmdQCFq85QUcXO4b7u9d6rpWFFS+Pfpkb/7qRNw6+wVvj32rUc5Zpyvgl6hesivvQv0PrGvytjRCCl2f1Jyo1n0dXhWJvY0lmQSlCQJBPOx6Z1JMJvTwZ0MX1spld43t68vfJJJ6Z3qdBs75MaUyXMVwTcA0rwlYwuetk+nn0a1Q5G+M2kleWx409DT/4++vRBCwtBNcMqj/rp62VJfeN9+fFP09zMDajzvdJY5l9C+Bw8mH2JO7hngH3NHo/zvnfHeGaT/aQnNO8mShbqsTsIkLPZzMzqLNR3vy2VpZ8cfsQvN3smf/9kXpnq/Ro34P7Au9jQ/wGtp5rXKrmXed3kVaURm5a8GV7ALdm2kHhwfTv4sL4np58OCeIo/83hd8WjGbR5B4M9GlX4994xsDOJOUUc+SceW0+v3joYtzt3Pm/vf9HaUXjMnauiVpDgGsAQzoMMWjdKjSS344lMq6HB17O+s2mmzO0K+6ONkZrBZh1AJBS8uHRD/Gy92Ju77mNKkOjkcRlFHAhp5g7vv6XnKLWPWtCHxt084+n9zfeTkftHGz49s5hWFsK5q34l7S8uvtt7x5wN73devPy/pfJKWl43/TqqNW42XpRkd+bPp3aTgAA6ORqz5r7R/H+TUFcE9SF9o429V4zuU8H7KwtzKobCMDV1pUlI5cQkx3DFye+aPD1pzNOczL9JDf0usHg28buP5NBUk4x1+rR/VPJ3saSe8b6szs6nePnsw1aHzDzALAzYSehaaHcH3R/o1d0ZhSUUlquYVr/jsSm53Pvd4dNsjNPS7L+ZBJ9O7ng61F/Kuem8HFzYPkdQ8nIL+Xubw9RWFr7TmLWFta8PFr74f/mvw1b+Xku9xz7Luyjr9MUwILeHRvXUmxLHG2tmNjbi/Unkyhvprw0hjLeZzwz/Gfw1cmvCM8Ib9C1qyNXY29lz4wAw08T/vVoAs52Vg2eTn3riK642FkZpRVgtgGgQlPBh0c/pJtLtyaN1FfOSb9usDfv3hjEv3GZPPJzaLMsw26JknKKOHoum+kDDLvysTYDfdrx0dxBhCXm8PDKY3X+3nu79ebuAXfzZ+yf7ErYpfdzrI1ai6WwxL5kFK721nRybR3pH4xtRmBn0vNLOWgmew/vjk5j2oe7Sc8v4clhT9Lerj3P7X1O78SCeaV5rI9bzzS/aY3uTq5Nfkk5G8KSuTqwc4MnIDjbWXPnaD82n04hIjnXoPUy2wCwPm49MdkxLAxaiHVtG73oITFLGwC6tLdn5sDOPHd1XzaeSmbJH2FmMw/akDac1C66mj6g+Ta6ntK3Ay/M7MeW8FRe/PNUnb/3+wLvo3u77ry470VyS+t/M5RWlPJbzG+E+IQQn2JJ747OBm/at1Yhvb1wtLE0i24gKSXv/BNJeFIun2yLwdXWledGPEdkViRfndQvseDfsX9TVF5klMHfDSeTKCqr4LrBjdvs5c7RvjjaWLJ0u2HzY5llACirKGNp6FL6uPVhqm/TsjkmZmu3MejSXrsL1d1j/LhvvD8/HjzHx9uMN/2qpdoQlkTvjs74ezZs682mun2kL/PH+fPd/rN8tTuu1vOsLa15ZfQrZBRn8M6hd+otd/PZzWSXZHN9jxuITM6jTyfV/aMvO2tLpvbryIawZErLW3Y30IHYTI4n5NDZ1Y6fDp7jfGYhE7tOrEosGJkZWef1UkpWRa6ir3vfRs8eqsuvRxPp5u7AkG6NW3/SzsGGW0d2468TFwy674NZBoC10WtJzE9k0eBFTV6ll5hVhLOdFS52/7UinrqyN9cO7sJ7m6NY+e+5plbXbKTkFnP4bFaz3v1X99SVvblqQCdeXR/O3ydqT4TVz6Mf8/rN47eY39iXuK/OMldHrsbH2YcudoEUlFa0qRlAhnB1YCdyisrYG2O8xUiGsGznGTycbPjp3hEg4IMt2sVvTw97GhdbF21XkKb2rqDQtFBismOMcvefkFXI/tgMrh3k3aTW5z1j/LGxtOCzHYZrBZhdACgsK+Tz458T3CGYUZ1HNbm8xOwiurS7eA9aIQRvXhfIhF6ePPvbSTbpkcCsNdhwMgkpm7f7pzoLC8G7Nw4kuFt7Hl0dyuH42vueHwh6AH9Xf17Y/0KtScBismI4mnqUG3reQGSK9q6pt2oBNMjYHp642Fm16G6g0xdy2RmVxp2j/fD1cGTeKF9+O5ZAVEoe7e3a838j/o/wzHBWhK2otYzVkatxsnZimt80g9fv92PaBY/XNrL7p5Knsy1zh3Xl92OJnM8srP8CPZhdAPgh/AcyijNYNHiRQfpyE7KK8G5/+Sbk1pYWfHrLYAZ4t+Ohlcfq/DBqLdaHJdOrgzPdvZq3+6c6O2tLvrw9mC7t7Lnnu8PE1tLctbW05aXRL5FSmMJ7h9+r8ZzVUauxtrDmmu7XEJ6UixDQs4PpXps5srGyYFr/Tmw6ndJiZ8d9sesMjjaW3Dq8GwAPjA/A0caKd/7RdvtM6TaFK3yv4LPjnxGddXlajKziLP6J/4cZATMMvoGUdtvHRIb7ueHj1vSy7xvvjxDaFo8hmFUAqJAVrAhbQYhPiMH2+a2pBVDJwcaKFfOG0qWdPXd9c4iolDyDPGdLlJpbzKH4TKY10+yfurR3tOGbO4diKQTzVhwiPb/mNQIDPQdyW5/bWB21moNJBy96rLCskD/P/Fm1nD8iKQ8/d0ccbFrF4vdmNWNgZ/JLytkRadzMlI1xPrOQP08kMXdY16qNVdo72jB/nD+bTqdwVLeQ7Znhz+Bs7cxze5+jXHPxdOM/Yv6gTFNmlO6fo+eyiUsv0Cv1gz46udpz/RAfVh8+z7mMprcCzCoApBelU1BWwEODDLPZS25xGXnF5VUDwDVxc7Th27uGYWttyR1f/8sFPVIZm6ONp5KREq4yUffPpbq5O/LlHcGk5BZzz7eHKSqt+e5z4aCFdHPpxvP7nqew7L83xMb4jeSX5XNTr5sA7TaQvdvYAjBDGeHvhrujDX/WMS5jKsv3xCGAu8denN7jrjF+eDjZ8NbGCKSUuNm58cyIZziVcYpvT31bdZ5GalgTtYbBXoPp3r67wev369EE7KwtDHpjtWhSDyyE4L3NdQ9s68OsAkBmcSYzAmbQo30Pg5RXOQW0cy0tgEo+bg58e+cw8ovLuePrf8kubNwS85Zs/ckkuns50aNDy/mQHNy1PR/OGcTxhGze3VTzP3Y7KzteGvUSF/IvXJQPfnXkarq3684gr0EUlJRzNrNQLQBrJCtLC6YP6MTW8BQKSmpfrNfcMgtK+fnQOa4J6kIn14vfw462ViwM6c6B2MyqbJpXdLuCyV0nszR0KbHZsQAcTDrIubxzRtn0pbisgj+PX+DKfh1xtmv8VPVLdXS1487RfvweeoFTF5qWsdWsAoCU0qCbvVTezdfWBVRd384ufHF7MGczCrnn28MtflpcQ6TllfBvXKbJBn/rcmX/jlw1oBNrjybU2gc9uMNgbu5zMz9F/MSRlCOcSj/FqYxT3NBTu5w/KiUPKVEzgJpgxsDOFJdp2BLecraa/G5/PMVlGu4f71/j43OHd8W7vT1v/ROBRiMRQvDsiGdxsHbgub3PUaGpYHXkatrbtmdKtykGr9/W8FRyi8sblPpBXw+MD8DFzoq3NjatFWBWAcDNzo0uTk0bSa+uchVwXV1A1Y0McOf1awdw+GwW24y8U09z+udUMpoW1P1zqTlDu5JdWMamOva5fXjQw3g7ebNk7xK+O/3dRcv5I5K1YzdqDUDjBXdrT0cXO/5qId1AhaXlfLsvnsl9vGpttdpaWfLo5J6EJeayIUw7k8/D3oOnhz3NifQTvHfkPbaf386s7rOwsaw/P1JD/Xo0gY4udozubvgU9a4O1jwY0p2dUWnsP5PR6HLMKgB4Oly+f2ZTJGYVYWNlgYej/mmgZwZ1xsnWqkUOiDXW+pNJ+Hs6ttgZMqMC3PFub8+qQ7WvyXCwduCl0S9xLu9c1XJ+ZxvtB0N4Ui5OtlZ6tfSUmllYCK4K7MTOyLQWkTBxzeEEsgrLuG98QJ3nzRrUhZ4dnHhnU2TVLnTT/aYT4hPCd6e/o0JWcEPPGwxev7S8EnZEpTFrUBcsjZRO+45RvnRyteMN3ThHY5hVALAUht3EI0E3A6ghKY+tLS0Y28OD7ZGprSJVRHp+CQdiM7hqQKcWmyLBwkJwU7APe2My6pz5MLTj0KpB3+ozOiKS8ujd0dls8tq3VDMGdqa0QmPydTHlFRq+3B3LkG7tGepb94YtlhaCxVf0Ji69gLVHEgDtOp/nRjyHi40Lo7uMxsfF8FvI/hGaSIVGNjr1gz7srC15ZHIPjp/PZmNY4/4mZhUADC0xq/YpoHUJ6e1FSm4J4UnmPy1006kUNBKmGTH1syFcH+yNhYDVh8/Xed6TQ5/kx+k/Vi3nl1ISrmYAGcRAb1d83OxNPhvo75NJJGQVcX89d/+VJvfxYnDXdny4JbpqHMnTwZM1M9bwxpg3DF6/8goNP/17jkBvV6NPqrhusDfdvZx4e1Nko7K2tu0AUMcagLpM6KntitreCrqB1p9Mws/DscXnyO/kas+EXl6sOXK+zn/o1pbWBHoGVv18IaeYvOJyNQPIAIQQXB3Ymb0x6WQWmGYmnJSSZTtj6e7lxKTeXnpdI4TgiSt7k5xbzHf746uOd3bqTDu7dgav42/HEolNK2DBBMNPK72UlaUFi6/oRWxaAWt0LZyGaLMBoLisgrS8Er0HgKvzcrGjfxcXsx8HyCwoZX9sBtMHdGyx3T/V3RjsQ0puCbui0/S+JiJJmzG0pQc4czEjsDMVGsmGMNO0AnZFpxOelMv8cf4N6tIb4e/OuJ6efLrjDLnFxhvDKCmv4IMt0QR6u3JFv4bl/W+sqX07MLhrOz7YElXrepnatNkAkKTb/rGxA4Mhvbw4cjaLnELTD4g11qZT2o3CW3r3T6VJfbzwcLLh53/r7gaqrnIGUM8WtL7BnPXp5EyAp6PJcgN9vvMMHVxsmRXU8L71J67oRXZhGV/uijVCzbRWHTpPYnYRj0/t1Ww3VUIInryyNym5JazYV3sm3Zq02QCg7yKw2kzo5YVGws4G3I0aWlFpBXlNuJv5+2QS3dwd6NfZPLpHrC0tuG6IN1sjUknN02//5tNJufi42Rt0IU5bJoRgxsDOHIzLJDW3effQPpGQzb4zGdw9xg8bq4Z/dPXv4srVgZ34andcvVuQNkZRaQUfb4thmJ8bY3sYfupnXYb7uzOxtxef7TjToIWqbTYAVC4CqykRnD6CfNrR3sGaHSZcD/DsbycZ8+Z2IpMbPhidVVDKvjMZTOvfcmf/1OSmYB8qNJJfjiTqdX5EUi59VP+/QV0d2BkptTcQzWnZzjM421kxd1jXRpfxv6m9KK3Q8Mm2y5PCNdW3++NJyyth8RXNd/df3RNX9iK/pLxB6aLbbABIyC7CQmiXVTeGpYVgfE9PdkSloTHB9pEajWRHlHZO9m3LDzY4MdTm0ylUaGSLXfxVG39PJ4b5ubHq0Ll6p+EWl1UQl16gUkAbWHcvJ/p0cmnWbqD49AI2hCVz24huTWrN+Xk4cmOwDz/9e85gKZVBm1fssx1nmNDLs96pqcbSu6MLswd1YcW+eL1zlrXZAJCYVUQHFzusLRv/Kwjp7UVmQSknEpuWj6MxolPzySwo5b5x/pRWaLhl+QFSGtAk//tkEj5u9vTvYn4fjnOG+hCfUVjvXrXRKfloJPRRKSAMbsbAThw9l01CluE+ROvyxe5YrC0tmDfat8llVSZTe39zVNMrpvPV7jhyisp4fGovg5XZGI9N6QkSPtii32vT69NPCHGlECJSCBEjhHiqhscfE0KcFkKcEEJsFUJ00x0PEUKEVvsqFkLM0j32jRAirtpjQXq/SgNIzC5s8srQcT08EQK2m6Ab6ECsdvn3rSO68c2dw8jML+W25Qf16v/LKdTu8DTdzLp/Kk3r3wlnWytWHap7MDhct4G2agEY3tUDOgPUuXOboaTmFbP2SALXDfbGy7lxLfbqOrraaTeNCU1sVPfppTLyS1i+O5bpAzrSv4trk8trCu/2Dtw6ohtrjyQQrUf6+noDgBDCElgKTAP6AnOFEH0vOe0YECylDATWAm8BSCm3SymDpJRBwESgENhU7brFlY9LKUPrra0BJWYXNWoKaHXtHW0Y5NPOJOsBDsZl0KWdPT5uDgT5tOPL24OJzyhk3opD9WZs3HQ6mXKNbJHJ3/Rhb2PJNYM6s/5kUp1pCSKS8rC3tqSrATbiUC7W1d2BgT7t+POE8buBvt0XT1mFhvnjak761hgPTAjAydaKp3490eSNbpbtPENRWYX27rsFWDixOw42Vrz9T/2J4vRpAQwDYqSUsVLKUuBn4JrqJ+g+6CvbggeAmtLfXQ9sqHaeyVRoJEnZxQbJDTOxtxcnEnKMMqugNlJKDsRmMtz/v77GUd09+GTuIE4m5jD/+8OUlNf+j3r9ySS6tLMn0Nu0dytNMWdoV0rKNawLrX0wODwpl54dnY2Wi6WtmxHYibDE3Fp3bTOE/JJyvt9/liv7dcTPw9Fg5bZzsOGt6wIJPZ/NwyuPUdHIcbzknGK+3X+W2YO86e7VMroa3RxtuE+3Ic6Rs3V3k+oTALoA1dvaCbpjtbkb2FDD8TnAykuOvarrNnpfCFFjRjYhxHwhxGEhxOG0NMNMuUzNK6ZcI5vcAgDtdFCAnVHNNx20sv9/hL/7Rcen9uvIW9cFsjcmg4dXHqtxxWxOURl7YtLNZvFXbfp3caVfZxd+rqUbSEpJRHIufdUCMKO5OrAzQmDUDKErD54jt7hc77QPDTFtQCeeu6ovm06n8OKfpxqV2+vjbdFIKXlksmH2KDGUu8f64eFky5sbIut8XQYdBBZC3AoEA29fcrwTMAD4p9rhp4HewFDADXiypjKllF9IKYOllMGenobJBlq5BsAQLYB+nV3wcrZt1m6gg7r+/xF+7pc9dt0Qb56f0Zd/TqXw1K8nL5uhtOV0CmUV5tv9U92coT6cupBLWA2D8Kl5JWQVlqkUEEbU0dWOod3c+MtI3UBnMwr4fNcZRvq7M9CnnVGe464xftw71o/v9p/l8wYuEDuXUciqQ+eZM7SrQfb7NSQHGysWTe7Bv/GZdX426RMAEoHq6fK8dccuIoSYDDwLzJRSXtofciPwm5SyqsNWSpkktUqAFWi7mppFYgM2gqmPEIIJvTzZFZXWqGRMjXEgNpPOrnb4uNVc/ztH+/HI5B6sPZLAK3+HX3QHsP5kEp1d7Qgy0huqOc0M6oKtlQU/15AmOlyXAkJtAmNcMwZ2Iiol3yCDqdWdzyxk7hcHqNBIXpjZz6BlX+rpaX24OrATb2yI4I86uhQv9cGWKKwsBQ9NNH7On8aYM9QHX3eHOjeN0ScAHAJ6CCH8hBA2aLty1lU/QQgxCPgc7Yd/TeFmLpd0/+haBQhtP8QsIEyPuhhEQlbDNoKpz8TeXuQVl3P0XLZByquLlJKDcRmM8Hevswtn0aQezBvly9d74/h4Wwygnau8OzqdaS049XNDuNpbM31AJ/44duGyHCiVKSBUC8C4pg3ohLWlYPHa43qvzq5PQlYhc744QEFpBT/cM5xeRg7iFhaCd28cyHA/Nx5fc5y9Men1XhOVksdvoYncMdIXL5emz0wyBmtLC/43tVfVe6Em9QYAKWU5sBBt9004sFpKeUoI8ZIQYqbutLcBJ2CNbkpnVYAQQviibUHsvKToH4UQJ4GTgAfwit6vrIkuZBfR3sEaBxsrg5Q3ursHVhaiWXYJO5OWT3p+6UUDwDURQrDk6r5cN9ib9zZH8c3eOLaGp1BaoWG6ATeoNrWbhvqQV1LO+ktWpUYk5dLZ1Q5XB5UCwpg8nGz59JYhRKfkM3vpPqL0mHpYlwvZRcz98gB5xWX8eM9w+nVunokKtlaWfHF7MH4ejtz//ZGqFmRt3tsUhaONlVHGJgzpqgGdeHhS7eMTeo0BSCnXSyl7SikDpJSv6o4tkVKu030/WUrZodqUzpnVro2XUnaRUmouKXOilHKAlLK/lPJWKaXxphJcwhBTQKtztrNmqK9bs2QH3R+rHdW/dAC4JhYWgjevG8CUvh144c/TvLspio4udgzyaW/sajab4X5u+Lo7sOqSfQLCk/LU/P9mMqVvB1bfN5LSCg3XfbqPPdH130HXJDmnmLlfHiC7sIzv7x7e7HPqXe2t+ebOYTjaWnHnikO1rqY9kZDNxlPJ3DPWj/aOht9K0pAsLESd01Pb5Ergxm4EU5eQ3p5EJOfpvQS7sQ7GZtDRxU7vue1WlhZ8PHcQowLcScgqYtqAjq1qZywhBDcN7cq/cZlV0xFLyis4k5avUkA3owHervz+4Gg6t7Nn3op/WV3PIr1LpeZqP/wz8kv57q5hRhv0rU/ndvZ8c9dQCkrKmbfi3xrXmbyzKYr2DtbcPcbPBDU0rDYXAKSUuo1gDDtqH6KbDroj0njTQSvn/4/wd2tQH76dtbZ5+2BIAPeONdximpbiuiHafVcrWwFnUgso10jV/9/MurSzZ80DIxkZ4M4Tv5zgrY0ReuXJSs0rZs6XB0jNLebbu4YyqKtpW6i9O7rw+W1DiEsvYP53F6+pORibwa6oNB6YENAqMsy2uQCQXVhGYWmFQbuAQJsgy7u9vVGng55JKyA9v0Sv7p9LOdlasfiK3o1Of92SeTnbMbG3F78cSaCsQkNEstoExlRc7Kz5et5Q5g7z4dMdZ3j452N1rrRNzy/h5i8PkpxTzDd3DWNIN9MkUrvUqO4evHPDQA7GZfK/1cfRaCRSSt7ZFEkHF1tuH+lr6ioahGFGQc2IIaeAVieEIKSXF78cTaCkvAJbK8NuYA/a9A+gzf2tXGzOUB82n05ha3gqEcl52FhZ4OtuuJWjiv6sLS14bfYAurk78saGCJJyivny9mDcLukvz8gv4ZYvD5KYVcQ3dw41WRbN2lwT1IWknGLe2BBB53b2jApw51B8Fi/P6o+dteHf36bQ5loAlVNAG7sPQF1CentSWFrBv/VkqWysA7GZdHCxxde9ZS06aQnG9/Skg4stqw6d06aA6OCEVRMyvSpNI4Tg/vEBLL15MCcTc5j96d6LUkZkFpRyy1cHOZtZwPJ5wS32pua+cf7cPrIbX+yK5dFVofi42XNTsE/9F5qJNvcOqWwBGKMrZKS/BzZWFmyPMPw4gJSSg7H1z/9vq6wsLbhhiA87o9IIPZet+v9biKsCO7Hy3hHkF5dz7Wf7+Dcuk+zCUm796iBx6QV8dftQRgU07+5ZDSGE4PkZ/biiXweyCst4ZFLPRu1G1lK1nleip8SsIuytLWlvhPnh9jaWjPR3N8p00Lj0AlLzShheQ/oHRevGYB80EvJKyumjpoC2GEO6tee3BaNxc7Th1q8Ocu2n+4hJy+fL24MZ08xbJzaGpYXgwzmD+PGe4Vw7uOF7EbdkbS4AXNCtATDWXfTE3l7EphcQn15g0HIPVM3/b1n9pC1JV3cHRnfXBki1CUzL0tXdgV8fGMWgru1IyCri81uHMK6nYXJ7NQc7a0tGd/doda3vNhcAtFNAjTcT5r/poIZtBRyMy8DT2dagKXFbo3vG+mt3OjPjVNetVTsHG366dwT7n55ISG8vU1dHoa0GACMMAFfq6u6Av6cj2wy4HkA7/1/1/+sjpJcXu5+YiEsrmKPdGllaCNydasz8rphAmwoAhaXlZBaUGrUFANoPoQOxGRSW1r0zl77OZhSSkluiun8URTGoNhUAKtM0GGMKaHUhvbwoLdew/0yGQcqr3P9XDQArimJIbSoAJBhwI5i6DPVrj6ONpcFWBR+IzcDDyZYAT9X/ryiK4bSpAFC1CtjILQBbK+2Mge0RaY3aZq46bf5/7f6/qv9fURRDalsBIKsIKwuBl7PxN3AI6e1FYnYR0alNy3J9LrOQpJziRuX/URRFqUvbCgDZRXR0tcOyGdIhT+ilneO8vYmbxFT2/49UA8CKohhY2woARtgHoDadXO3p08mlyeMAB2Mz8XCyIcDTyUA1UxRF0WpTAeCCkdcAXCqklyeH47PILb58Uwl9VM7/H+6n5v8rimJ4bSYddFmFhuTcYrybMR9+SG8vPt1xhr26jdgbKiGriAs5xTygun+UBiorKyMhIYHiYsNs1K6YBzs7O7y9vbG21m8hZJsJAMk5xWik8WcAVTfIpx0udlZsOp3SqACwP1bl/1caJyEhAWdnZ3x9fVXrsY2QUpKRkUFCQgJ+fvptV9lmuoD+2wim+XLpW1lacO1gb347ltioweADsRm4OdrQw0v1/ysNU1xcjLu76jpsS4QQuLu7N6jV13YCQFbzrAG41FPTetOnkwuPrArlfGZhg649GJvJcD81/19pHPXvpu1p6N+87QQAXQugk6vx1wBUZ2dtybJbB6ORkgd+PFLn/qjVnc8sJDG7SM3/VxTFaNpOAMgqwsPJ1iR7eXZzd+T9G4MIS8zlxT9P6XXNwbjK/P8qACjmKzk5mTlz5hAQEMCQIUOYPn06UVFR9O/f39RVUzCzAJBb1LjplGD8NND1mdy3Aw+GBLDy3/OsPny+3vMPxGbQ3sFa9f8rZktKyezZs5kwYQJnzpzhyJEjvP7666SkpJi6aoqOWQWAzMLSRl+bmF3UrFNAa/LYlF6M7u7Oc7+HEZaYU+e5lfP/LZph1bKiGMP27duxtrbm/vvvrzo2cOBAfHz+21S9uLiYO++8kwEDBjBo0CC2b98OwKlTpxg2bBhBQUEEBgYSHR0NwA8//FB1/L777qOiQr8uVaVmZjUNNL+4nMLSchxsGlZtjUaSmF3ElL4djFQz/VTuLXr1R3tY8ONR/lw4Btca9iZOyCokIauIu8foN5VLUery4p+nOH0h16Bl9u3swvMz+tV5TlhYGEOGDKnznKVLlyKE4OTJk0RERDB16lSioqJYtmwZixYt4pZbbqG0tJSKigrCw8NZtWoVe/fuxdramgULFvDjjz9y++23G/KltSl6tQCEEFcKISKFEDFCiKdqePwxIcRpIcQJIcRWIUQ33fEQIURota9iIcQs3WN+QoiDujJXCSFs6quHBHZHpzfsFQIZBaWUlmuaLQ1EXTycbPn01sEk5RTx2OpQNJrLs4UejFX9/0rbsGfPHm699VYAevfuTbdu3YiKimLkyJG89tprvPnmm5w9exZ7e3u2bt3KkSNHGDp0KEFBQWzdupXY2FgTvwLzVu+ttBDCElgKTAESgENCiHVSytPVTjsGBEspC4UQDwBvATdJKbcDQbpy3IAYYJPumjeB96WUPwshlgF3A5/VVRcLIdhyOoUr+nVsyGustgbA9AEAYHDX9vzfVX15ft0pPtt5hgdDul/0+IHYDNo5WNOrg9rYXGm6+u7UjaVfv36sXbu2UdfefPPNDB8+nL///pvp06fz+eefI6Xkjjvu4PXXXzdwTdsufVoAw4AYKWWslLIU+Bm4pvoJUsrtUsrKSe4HAO8ayrke2KALEgKYCFT+6/gWmFVfRVzsrNgWkUpFDXfNdTHVGoC63D6yGzMHdubdTZHsuaRVczAuk2G+bqr/XzFrEydOpKSkhC+++KLq2IkTJzh//r9JEGPHjuXHH38EICoqinPnztGrVy9iY2Px9/fn4Ycf5pprruHEiRNMmjSJtWvXkpqqXVSZmZnJ2bNnm/dFtTL6BIAuQPVpKwm6Y7W5G9hQw/E5wErd9+5AtpSyctPc+soEwNnemoyCUkLPZ9Vb6eoSs7WxqSUFACEEr187gABPJx7++VjVdpUXsos4l1moun8UsyeE4LfffmPLli0EBATQr18/nn76aTp2/K8Fv2DBAjQaDQMGDOCmm27im2++wdbWltWrV9O/f3+CgoIICwvj9ttvp2/fvrzyyitMnTqVwMBApkyZQlJSkglfofkz6CCwEOJWIBgYf8nxTsAA4J9GlDkfmA/g07UbNhaCzadTGdJN/wRpiVlFONtZ4WKnX4Kk5uJoa8Wy24Yw82PtoPDq+0ZyMK4y/49KAKeYv86dO7N69erLjoeFhQHa5GUrVqy47PGnnnqKp566bLiRm266iZtuusnwFW2j9GkBJAI+1X721h27iBBiMvAsMFNKWXLJwzcCv0kpKyfyZwDthBCVAajGMgGklF9IKYOllMFenh4M93djS3jD5hEnZjffPgANFeDpxNs3DCT0fDav/H2aA2cycbW3pk9HF1NXTVGUVk6fAHAI6KGbtWODtitnXfUThBCDgM/RfvjXlPVsLv91/yC1G+VuRzsuAHAH8Ic+FZ7cpwMxqfnEpxfoczqgTavcUgMAwPQBnbhnjB/f7T/LuuMXGOan+v8VRTG+egOArp9+Idrum3BgtZTylBDiJSHETN1pbwNOwBrddM+qACGE8EXbgth5SdFPAo8JIWLQjgks16fCk/to5/I3pBVg6lXA+nhyWm+G+bpRVFbBcD/V/aMoivHpNQYgpVwPrL/k2JJq30+u49p4ahjglVLGop1h1CA+bg707ujMlvAU7hnrX+/5ucVl5BWXt+gWAIC1pQWf3DyINzZEcHVgZ1NXR1GUNsCsUkFUmtynA4fis8jWIzVE5eyalt4CAPByseO9m4Lo2MwZSxVFaZvMMgBM6uNFhUayIzKt3nOr1gC08BaAoihKczPLADDQux0eTrZs1mMcINGMWgCKoijNySwDgIWFYHIfL3ZGplFarqnz3MSsImysLPBwtG2m2imKAmBpaUlQUBD9+/fnhhtuoLCwYTviVTdv3ryqtBL33HMPp0+frvXcHTt2sG/fvgY/h6+vL+npdeca+/333xFCEBERUXUsPj4ee3t7goKC6Nu3L7fffjtlZWVVdbn66qsB+OabbxBCsGXLlsvKq54yIz09HWtra5YtW9bg19BQZhkAQDsOkF9SXrVwqjYJujUAalqlojQve3t7QkNDCQsLw8bG5rIPtPLy8lqurNtXX31F3759a328sQFAHytXrmTMmDGsXLnyouMBAQGEhoZy8uRJEhISalz8BjBgwAB+/vnni8obOHDgReesWbOGESNGXPYcxmBW6aCrG93dAztrC7acTmFsD89az0ts4WsAFMXoNjwFyScNW2bHATDtDb1PHzt2LCdOnGDHjh0899xztG/fnoiICMLDw3nqqafYsWMHJSUlPPjgg9x3331IKXnooYfYvHkzPj4+2Nj8lyx4woQJvPPOOwQHB7Nx40aeeeYZKioq8PDwYPny5SxbtgxLS0t++OEHPv74Y3r37s3999/PuXPnAPjggw8YPXo0GRkZzJ07l8TEREaOHIl2eVLt8vPz2bNnD9u3b2fGjBm8+OKLl51jaWnJsGHDSEyscV0rY8eOZffu3ZSVlVFSUkJMTAxBQUEXnbNy5Ureffddbr75ZhISEvD2rim1mmGYbQvA3saSMd092RKeWucfLjG7iM7t1KwaRTGV8vJyNmzYwIABAwA4evQoH374IVFRUSxfvhxXV1cOHTrEoUOH+PLLL4mLi+O3334jMjKS06dP891339V4R5+Wlsa9997LL7/8wvHjx1mzZg2+vr7cf//9PProo4SGhjJ27FgWLVrEo48+yqFDh/jll1+45557AHjxxRcZM2YMp06dYvbs2VUBojZ//PEHV155JT179sTd3Z0jR45cdk5xcTEHDx7kyiuvrLEMIQSTJ0/mn3/+4Y8//mDmzJkXPX7+/HmSkpIYNmwYN954I6tWrdLrd9xYZtsCAJjS14st4SlEJOfRp9PlqROKyypIyyuhSzsHE9ROUVqIBtypG1JRUVHV3e3YsWO5++672bdvH8OGDcPPT7vZ0aZNmzhx4kRVH3hOTg7R0dHs2rWLuXPnYmlpSefOnZk4ceJl5R84cIBx48ZVleXmVvMCyi1btlw0ZpCbm0t+fj67du3i119/BeCqq66iffv2db6elStXsmjRIgDmzJnDypUrqza8OXPmDEFBQcTFxXHVVVcRGBhYazlz5szho48+Iicnh3fffZfXXnut6rFVq1Zx4403Vp1311138b///a/OejWFWQeAib07IMRJtpxOqTEAJOUUA2oGkKKYQuUYwKUcHR2rvpdS8vHHH3PFFVdcdM769esvvazRNBoNBw4cwM6u8T0BmZmZbNu2jZMnTyKEoKKiAiEEb7/9NvDfGEB6ejqjR49m3bp1l93dVxo2bBgnT57EwcGBnj17XvTYypUrSU5OrkqRfeHCBaKjo+nRo0ej614Xs+0CAvB0tiXIp12taSHUGgBFadmuuOIKPvvss6pZM1FRURQUFDBu3DhWrVpFRUUFSUlJVXsFVzdixAh27dpFXFwcoP2QBnB2diYvL6/qvKlTp/Lxxx9X/VwZlMaNG8dPP/0EwIYNG8jKqj3N/Nq1a7nttts4e/Ys8fHxnD9/Hj8/P3bv3n3ReR4eHrzxxhv1blrzxhtvXHTnX/na8/PzSUxMJD4+nvj4eJ5++mmjDgabdQAA7Wyg4wk5pOQWX/ZY5Spgb9UCUJQW6Z577qFv374MHjyY/v37c99991FeXs7s2bPp0aNH1bTKkSNHXnatp6cnX3zxBddeey0DBw6sShM9Y8YMfvvtN4KCgti9ezcfffQRhw8fJjAwkL59+1bNRnr++efZtWsX/fr149dff6Vr16611nPlypXMnj37omPXXXddjR/Os2bNorCw8LLgUN20adMICQlp9HMYiqhv5LslCQ4OlocPH77oWFRKHlPf38Vrswdw8/CL/4DvbY7ik23RRL4yDWtLs491iqK38PBw+vTpY+pqKCZQ099eCHFEShl86blm/6nYw8uJrm4ONXYDJWYV0cHFTn34K4qi1MCsB4FBO61qUh8vfjx4jsLSchxs/ntJidmFqv9fURS9ZWRkMGnSpMuOb926FXf31rdNq9kHAIApfTqwYm88u6PTuaLff/uNJmYXMbhr3VO7FEVRKrm7u9c4c6m1ahV9I0P93HC2s2LL6f+6gSo0kqTsYjqrFoCiKEqNWkUAsLa0IKSXF9siUqnQaAe1U/OKKddI1QWkKIpSi1YRAAAm9+1ARkEpoeezgWprANQUUEVRlBq1mgAwvqcnVhaiajZQ5T4A3qoFoCiKUqNWEwBc7a0Z7u9WNQ6gNoJRFNNqbfsBvPrqq/Tr14/AwECCgoI4ePBg1WPl5eV4enry1FNPXXTNhAkT6NWrFwMHDmTo0KEXDTBXfz4hBLfeeutl5VXuJVBp1qxZjBgxosGvrTatJgCAdlVwdGo+8ekFJGYV0d7B+qJpoYqiNJ/WtB/A/v37+euvvzh69CgnTpxgy5Yt+Pj4VD2+efNmevbsyZo1ay7LTvzjjz9y/PhxFixYwOLFi2ss39HRkbCwMIqKiqrK69Kly0XnZGdnc+TIEXJycoiNjTXI62pVn46T+3TgxT9PsyU8hcTsInX3ryjAm/++SURmRP0nNkBvt948OexJvc839/0AkpKS8PDwwNZWu7Ogh4fHRY9XZgr97LPP2L9/P6NGjbqsjJEjR1Ylj6vJ9OnT+fvvv7n++utZuXIlc+fOvSidxK+//sqMGTPo0KEDP//8M88884x+v/w6tKoWgI+bA707OmsDgNoIRlFahNawH8DUqVM5f/48PXv2ZMGCBezcubPqseLiYrZs2cKMGTOYO3durbl7Nm7cyKxZs2p9jjlz5vDzzz9TXFzMiRMnGD58+EWPVwaFup6joVpVCwC0rYDPdp7BykLUuVOYorQVDblTN6TWtB+Ak5MTR44cYffu3Wzfvp2bbrqJN954g3nz5vHXX38REhKCvb091113HS+//DIffPABlpaWANxyyy2UlpaSn59f5yKzwMBA4uPjWblyJdOnT7/osZSUFKKjoxkzZgxCCKytrQkLC6N///61lqePVhcAJvXx4pPtMVRopNoJTFFMqDXtBwDaQe0JEyYwYcIEBgwYwLfffsu8efNYuXIle/bswdfXF9Cmk9i2bRtTpkwBtGMAQ4YMYfHixTz00ENVQacmM2fO5PHHH2fHjh1kZPy33/nq1avJysqqCna5ubmsXLmSV199tUmvqVV1AQEM9G6Hh5O2n06lgVaUls1c9gOIjIwkOjr6ojK6detGbm4uu3fv5ty5c1U5/JcuXXpZF40QgpdffpkDBw4QEVH7eMxdd93F888/X9VdVmnlypVs3Lix6jmOHDly0ebyjdXqAoCFhWByHy8AtRWkorRw5rIfQH5+PnfccQd9+/YlMDCQ06dP88ILL/Dbb78xceLEqsFhgGuuuYY///yTkpKSi8qwt7fnf//7X50Dwd7e3jz88MMXHYuPj+fs2bMXTf/08/PD1dX1oqmojWH2+wHU5GRCDm9sDOer24dib2PZDDVTlJZF7QfQdhl8PwAhxJVCiEghRIwQ4qkaHn9MCHFaCHFCCLFVCNGt2mNdhRCbhBDhunN8dce/EULECSFCdV9BDXydtRrg7cqP94xQH/6Koih1qHcQWAhhCSwFpgAJwCEhxDopZfWleMeAYClloRDiAeAt4CbdY98Br0opNwshnABNtesWSynXGuKFKIqiNJXaD+Byw4AYKWUsgBDiZ+AaoCoASCmrj9AcAG7VndsXsJJSbtadl2+geiuKUg8pJUIIU1fDrJj7fgAN7dLXpwuoC3C+2s8JumO1uRvYoPu+J5AthPhVCHFMCPG2rkVR6VVdt9H7Qgjby4tSFKUx7OzsyMjIaPAHgmK+pJRkZGQ0aLqrQdcBCCFuBYKB8dXKHwsMAs4Bq4B5wHLgaSAZsAG+AJ4EXqqhzPnAfKDOUXpFUf7j7e1NQkICaWlppq6K0ozs7Ozw9vbW+3x9AkAi4FPtZ2/dsYsIISYDzwLjpZSV858SgNBq3Ue/AyOA5VLKJN05JUKIFcDjNT25lPILtAGC4OBgdTujKHqwtrauWjSkKLXRpwvoENBDCOEnhLAB5gDrqp8ghBgEfA7MlFKmXnJtOyFEZU6GiejGDoQQnXT/F8AsIKwJr0NRFEVpoHpbAFLKciHEQuAfwBL4Wkp5SgjxEnBYSrkOeBtwAtboBp3OSSlnSikrhBCPA1t1H/RHgC91Rf+oCwwCCAXuN/BrUxRFUerQKheCKYqiKP+pbSGYWQUAIUQeEKnn6a5ATgOKb8j5xiy7JdWlJZXtAdS+XVPTym4pv5OWVBdVdvOWbey69JJSOl92VEppNl9ou5z0PfeLBpat9/nGLLsl1aWFla3+9qrsVlt2M9SlxvdPq0sGV82fRjzfmGU39Py2UnZDtKR6m2tdVNnNW3ZDzzfI+8fcuoAOyxr6sZTWT/3tFaXxanv/mFsL4AtTV0AxGfW3V5TGq/H9Y1YtAEVRFMVwzK0F0GYJIXyEENt1KbVPCSEWVXvsISFEhO74W6asZ0tXU2pzIcRyIcRxXV6qtbqstUothBBfCyFShRBh1Y65CSE2CyGidf+vfYPdNq6W39+qaqnx44UQoc1SF9UCMA+6ldOdpJRHhRDOaBfVzQI6oE3BcZWUskQI4SUvXo2t6OgSEUZRLbU5MBdIkFLm6s55D0iVUr5hsoq2cEKIcUA+8J2Usr/u2FtAppTyDV1gbS+lNM1u9C1cTb+/Sx5/F8iRUl6WG83QVAvATEgpk6SUR3Xf5wHhaLOyPgC8IXX5l9SHf52qUptLKUuBn4Frqn34C8AeUHdFdZBS7gIyLzl8DfCt7vtv0d6cKDWo5fcHVP0bvBFYWdPjhqYCgBnS7ao2CDiINuX2WCHEQSHETiHEUJNWrmWrNbW5LiFhMtAb+PjyS5V6dJD/JXhMRtsyVRpuLJAipYyu90wDUAHAzOj6p38BHtHduVoBbmizrC4GVgu1C0iDSSnvBDqjbVndVM/pSh2ktl9ZtaIaZy7NdPcPKgCYFSGENdoP/x+llL/qDicAv0qtf9Fuuelhqjq2cHWmNpdSVqDtFrqumevVGqRUy/DbCVBdkQ0khLACrkW7b0qzUAHATOju6pcD4VLK96o99DsQojunJ9oNdvTNmdPW1JjaXAjRHap+xzOBCBPW0VytA+7QfX8H8IcJ62KuJgMRUsqE5npCg+4IphjVaOA24GS1KWLPAF8DX+umlJUCd0g1tatGsobU5mi7fHYLIVzQpiY/jnZgXamFEGIlMAHwEEIkAM8Db6DtfrwbOIt2IFOpQU2/PynlcrQ3JM3W/QNqGqiiKEqbpbqAFEVR2igVABRFUdooFQAURVHaKBUAFEVR2igVABRFUdooFQAURVHaKBUAFEVR2igVABRFUdooFQAURVHaKBUAFEVR2igVABRFUdooFQAURVHaKBUAFEVR2igVABRFUdooFQAURVHaKBUAFEVR2igVAJQWQwjhLYT4QwgRLYQ4I4T4ULd1Y23nPyKEcGjOOipKa6ICgNIi6Pbj/RX4XUrZA+gJOAGv1nHZI4AKAIrSSGpLSKVFEEJMQrs36rhqx1yAOKAr8CJwJaABvkS7f+87QCSQLqUMafZKK4qZU5vCKy1FP+BI9QNSylwhxDngHsAXCNJt7O4mpcwUQjwGhEgp05u/uopi/lQXkGIOJgCfSynLAaSUmaatjqK0DioAKC3FaWBI9QO6LqCupqmOorR+KgAoLcVWwEEIcTuAEMISeBf4BvgHuE8IYaV7zE13TR7g3PxVVZTWQQUApUWQ2tkIs4EbhBDRQBRQDDwDfAWcA04IIY4DN+su+wLYKITYboIqK4rZU7OAFEVR2ijVAlAURWmjVABQFEVpo1QAUExCCOEjhNguhDgthDglhFikO+4mhNisSwexWQjRXnf8FiHECSHESSHEPiHEwLrKURSlfmoMQDEJIUQnoJOU8qgQwhntIrBZwDwgU0r5hhDiKaC9lPJJIcQoIFxKmSWEmAa8IKUcXls5UsrTJnlhimJGVAtAMQkpZZKU8qju+zwgHOgCXAN8qzvtW7RBASnlPilllu74AcC7nnIURamHCgCKyQkhfIFBwEGgg5QySfdQMtChhkvuBjbUU46iKPVQuYAUkxJCOAG/AI/ocv9UPSallEIIecn5IWgDwJi6yjF6xRWlFVAtAMVkhBDWaD+0f5RS/qo7nKLr168cJ0itdn4g2kVh10gpM+opR1GUeqgAoJiELv//crQDu+9Ve2gdcIfu+zuAP3Tnd0W7X8BtUsooPcpRFKUeahaQYhJCiDHAbuAk2hz/oE37cBBYjTYJ3FngRl3q56+A63THAMqllMG1lSOlXN88r0RRzJcKAIqiKG2U6gJSFEVpo1QAUBRFaaNUAFAURWmjVABQFEVpo1QAUBRFaaNUAFCUWgghKoQQoboso8eFEP8TQtT5nhFC+Aohbq7rHEVpKVQAUJTaFUkpg6SU/YApwDTg+Xqu8eW/LSsVpUVT6wAUpRZCiHwppVO1n/2BQ4AH0A34HnDUPbxQSrlPCHEA6APEoc1m+hHwBjABsAWWSik/b7YXoSh1UAFAUWpxaQDQHcsGegF5gEZKWSyE6AGs1K1MngA8LqW8Wnf+fMBLSvmKEMIW2AvcIKWMa8aXoig1UtlAFaVxrIFPhBBBQAXQs5bzpgKBQojrdT+7Aj3QthAUxaRUAFAUPem6gCrQZih9HkgBBqIdSyuu7TLgISnlP81SSUVpADUIrCh6EEJ4AsuAT6S239QVSJJSaoDbAEvdqXmAc7VL/wEe0KWsRgjRUwjhiKK0AKoFoCi1sxdChKLt7ilHO+hbmXL6U+AXIcTtwEagQHf8BFAhhDgOfAN8iHZm0FFd6uo0dNtcKoqpqUFgRVGUNkp1ASmKorRRKgAoiqK0USoAKIqitFEqACiKorRRKgAoiqK0USoAKIqitFEqACiKorRRKgAoiqK0Uf8PH6K2B7qAzDEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data[['Close','Predicted_ARIMA','Predicted_SARIMA']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b91125f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007845882637084614 6.155787435490583e-05\n"
     ]
    }
   ],
   "source": [
    "# Checking Mean Squared Error and RMSE Rate For Sarima\n",
    "from sklearn.metrics import mean_squared_error\n",
    "Y_true = test_data['Close']\n",
    "Y_pred = test_data['Predicted_SARIMA']\n",
    "rmse = mean_squared_error(Y_true,Y_pred, squared = False)\n",
    "mse = mean_squared_error(Y_true,Y_pred)\n",
    "print(rmse,mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3c8eda0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.006365353217647374 4.0517721585413775e-05\n"
     ]
    }
   ],
   "source": [
    "# Checking Error Rate For Arima\n",
    "from sklearn.metrics import mean_squared_error\n",
    "Y_true = test_data['Close']\n",
    "Y_pred = test_data['Predicted_ARIMA']\n",
    "rmse = mean_squared_error(Y_true,Y_pred, squared = False)\n",
    "mse = mean_squared_error(Y_true,Y_pred)\n",
    "print(rmse,mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23d3421c",
   "metadata": {},
   "source": [
    "# FbProphet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a3ca474c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "df = yfinance.download('BTC-USD',start = '2013-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9c340dd2",
   "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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-09-17 00:00:00+00:00</th>\n",
       "      <td>465.864014</td>\n",
       "      <td>468.174011</td>\n",
       "      <td>452.421997</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>21056800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-18 00:00:00+00:00</th>\n",
       "      <td>456.859985</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>413.104004</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>34483200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-19 00:00:00+00:00</th>\n",
       "      <td>424.102997</td>\n",
       "      <td>427.834991</td>\n",
       "      <td>384.532013</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>37919700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-20 00:00:00+00:00</th>\n",
       "      <td>394.673004</td>\n",
       "      <td>423.295990</td>\n",
       "      <td>389.882996</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>36863600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-21 00:00:00+00:00</th>\n",
       "      <td>408.084991</td>\n",
       "      <td>412.425995</td>\n",
       "      <td>393.181000</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>26580100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Date                                                                        \n",
       "2014-09-17 00:00:00+00:00  465.864014  468.174011  452.421997  457.334015   \n",
       "2014-09-18 00:00:00+00:00  456.859985  456.859985  413.104004  424.440002   \n",
       "2014-09-19 00:00:00+00:00  424.102997  427.834991  384.532013  394.795990   \n",
       "2014-09-20 00:00:00+00:00  394.673004  423.295990  389.882996  408.903992   \n",
       "2014-09-21 00:00:00+00:00  408.084991  412.425995  393.181000  398.821014   \n",
       "\n",
       "                            Adj Close    Volume  \n",
       "Date                                             \n",
       "2014-09-17 00:00:00+00:00  457.334015  21056800  \n",
       "2014-09-18 00:00:00+00:00  424.440002  34483200  \n",
       "2014-09-19 00:00:00+00:00  394.795990  37919700  \n",
       "2014-09-20 00:00:00+00:00  408.903992  36863600  \n",
       "2014-09-21 00:00:00+00:00  398.821014  26580100  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f219d21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dfb93896",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = df[['Date','Close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "4d0dc1ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.rename(columns = {\"Date\":\"ds\",'Close':'y'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "02eaa24b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2014-09-17\n",
       "1       2014-09-18\n",
       "2       2014-09-19\n",
       "3       2014-09-20\n",
       "4       2014-09-21\n",
       "           ...    \n",
       "2956    2022-10-21\n",
       "2957    2022-10-22\n",
       "2958    2022-10-23\n",
       "2959    2022-10-24\n",
       "2960    2022-10-25\n",
       "Name: ds, Length: 2961, dtype: object"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['ds'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "125d4842",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['ds'] = train_data['ds'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "137ca16f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reducing Training Data\n",
    "test_data = train_data.loc[len(train_data)-30:]\n",
    "train_data = train_data.loc[:len(train_data)-30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4272a5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30, 2) (2932, 2)\n"
     ]
    }
   ],
   "source": [
    "print(test_data.shape,train_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "32c58498",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "19:51:18 - cmdstanpy - INFO - Chain [1] start processing\n",
      "19:51:19 - cmdstanpy - INFO - Chain [1] done processing\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<prophet.forecaster.Prophet at 0x1b929ec6e80>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from prophet import Prophet\n",
    "model = Prophet()\n",
    "model.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4bc7e214",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making prediction for 30 days\n",
    "future = model.make_future_dataframe(periods=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "48458bf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>2022-10-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>2022-10-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>2022-10-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>2022-10-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>2022-10-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds\n",
       "2957 2022-10-22\n",
       "2958 2022-10-23\n",
       "2959 2022-10-24\n",
       "2960 2022-10-25\n",
       "2961 2022-10-26"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "0bf984e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>2022-10-22</td>\n",
       "      <td>30411.798957</td>\n",
       "      <td>24489.266604</td>\n",
       "      <td>35746.791078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>2022-10-23</td>\n",
       "      <td>30428.217965</td>\n",
       "      <td>24731.658244</td>\n",
       "      <td>35816.458778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>2022-10-24</td>\n",
       "      <td>30463.228496</td>\n",
       "      <td>24976.570896</td>\n",
       "      <td>36138.954362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>30470.538365</td>\n",
       "      <td>24549.517535</td>\n",
       "      <td>36476.469168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>2022-10-26</td>\n",
       "      <td>30513.786966</td>\n",
       "      <td>25311.993544</td>\n",
       "      <td>36038.231840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds          yhat    yhat_lower    yhat_upper\n",
       "2957 2022-10-22  30411.798957  24489.266604  35746.791078\n",
       "2958 2022-10-23  30428.217965  24731.658244  35816.458778\n",
       "2959 2022-10-24  30463.228496  24976.570896  36138.954362\n",
       "2960 2022-10-25  30470.538365  24549.517535  36476.469168\n",
       "2961 2022-10-26  30513.786966  25311.993544  36038.231840"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast = model.predict(future)\n",
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5ebba88f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = model.plot(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a3c0ccdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Date',inplace = True)\n",
    "df['Close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "83c842ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>trend</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>additive_terms</th>\n",
       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
       "      <th>weekly</th>\n",
       "      <th>weekly_lower</th>\n",
       "      <th>weekly_upper</th>\n",
       "      <th>yearly</th>\n",
       "      <th>yearly_lower</th>\n",
       "      <th>yearly_upper</th>\n",
       "      <th>multiplicative_terms</th>\n",
       "      <th>multiplicative_terms_lower</th>\n",
       "      <th>multiplicative_terms_upper</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>2022-10-22</td>\n",
       "      <td>28228.662385</td>\n",
       "      <td>24489.266604</td>\n",
       "      <td>35746.791078</td>\n",
       "      <td>28228.662385</td>\n",
       "      <td>28229.156258</td>\n",
       "      <td>2183.136572</td>\n",
       "      <td>2183.136572</td>\n",
       "      <td>2183.136572</td>\n",
       "      <td>3.608034</td>\n",
       "      <td>3.608034</td>\n",
       "      <td>3.608034</td>\n",
       "      <td>2179.528538</td>\n",
       "      <td>2179.528538</td>\n",
       "      <td>2179.528538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30411.798957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>2022-10-23</td>\n",
       "      <td>28191.597368</td>\n",
       "      <td>24731.658244</td>\n",
       "      <td>35816.458778</td>\n",
       "      <td>28191.462691</td>\n",
       "      <td>28196.428564</td>\n",
       "      <td>2236.620597</td>\n",
       "      <td>2236.620597</td>\n",
       "      <td>2236.620597</td>\n",
       "      <td>-9.364699</td>\n",
       "      <td>-9.364699</td>\n",
       "      <td>-9.364699</td>\n",
       "      <td>2245.985296</td>\n",
       "      <td>2245.985296</td>\n",
       "      <td>2245.985296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30428.217965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>2022-10-24</td>\n",
       "      <td>28154.532352</td>\n",
       "      <td>24976.570896</td>\n",
       "      <td>36138.954362</td>\n",
       "      <td>28150.088541</td>\n",
       "      <td>28166.382251</td>\n",
       "      <td>2308.696144</td>\n",
       "      <td>2308.696144</td>\n",
       "      <td>2308.696144</td>\n",
       "      <td>3.801889</td>\n",
       "      <td>3.801889</td>\n",
       "      <td>3.801889</td>\n",
       "      <td>2304.894255</td>\n",
       "      <td>2304.894255</td>\n",
       "      <td>2304.894255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30463.228496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>28117.467335</td>\n",
       "      <td>24549.517535</td>\n",
       "      <td>36476.469168</td>\n",
       "      <td>28103.832760</td>\n",
       "      <td>28138.422756</td>\n",
       "      <td>2353.071030</td>\n",
       "      <td>2353.071030</td>\n",
       "      <td>2353.071030</td>\n",
       "      <td>-3.219573</td>\n",
       "      <td>-3.219573</td>\n",
       "      <td>-3.219573</td>\n",
       "      <td>2356.290603</td>\n",
       "      <td>2356.290603</td>\n",
       "      <td>2356.290603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30470.538365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2961</th>\n",
       "      <td>2022-10-26</td>\n",
       "      <td>28080.402318</td>\n",
       "      <td>25311.993544</td>\n",
       "      <td>36038.231840</td>\n",
       "      <td>28052.624346</td>\n",
       "      <td>28110.885729</td>\n",
       "      <td>2433.384648</td>\n",
       "      <td>2433.384648</td>\n",
       "      <td>2433.384648</td>\n",
       "      <td>33.096176</td>\n",
       "      <td>33.096176</td>\n",
       "      <td>33.096176</td>\n",
       "      <td>2400.288472</td>\n",
       "      <td>2400.288472</td>\n",
       "      <td>2400.288472</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30513.786966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds         trend    yhat_lower    yhat_upper   trend_lower  \\\n",
       "2957 2022-10-22  28228.662385  24489.266604  35746.791078  28228.662385   \n",
       "2958 2022-10-23  28191.597368  24731.658244  35816.458778  28191.462691   \n",
       "2959 2022-10-24  28154.532352  24976.570896  36138.954362  28150.088541   \n",
       "2960 2022-10-25  28117.467335  24549.517535  36476.469168  28103.832760   \n",
       "2961 2022-10-26  28080.402318  25311.993544  36038.231840  28052.624346   \n",
       "\n",
       "       trend_upper  additive_terms  additive_terms_lower  \\\n",
       "2957  28229.156258     2183.136572           2183.136572   \n",
       "2958  28196.428564     2236.620597           2236.620597   \n",
       "2959  28166.382251     2308.696144           2308.696144   \n",
       "2960  28138.422756     2353.071030           2353.071030   \n",
       "2961  28110.885729     2433.384648           2433.384648   \n",
       "\n",
       "      additive_terms_upper     weekly  weekly_lower  weekly_upper  \\\n",
       "2957           2183.136572   3.608034      3.608034      3.608034   \n",
       "2958           2236.620597  -9.364699     -9.364699     -9.364699   \n",
       "2959           2308.696144   3.801889      3.801889      3.801889   \n",
       "2960           2353.071030  -3.219573     -3.219573     -3.219573   \n",
       "2961           2433.384648  33.096176     33.096176     33.096176   \n",
       "\n",
       "           yearly  yearly_lower  yearly_upper  multiplicative_terms  \\\n",
       "2957  2179.528538   2179.528538   2179.528538                   0.0   \n",
       "2958  2245.985296   2245.985296   2245.985296                   0.0   \n",
       "2959  2304.894255   2304.894255   2304.894255                   0.0   \n",
       "2960  2356.290603   2356.290603   2356.290603                   0.0   \n",
       "2961  2400.288472   2400.288472   2400.288472                   0.0   \n",
       "\n",
       "      multiplicative_terms_lower  multiplicative_terms_upper          yhat  \n",
       "2957                         0.0                         0.0  30411.798957  \n",
       "2958                         0.0                         0.0  30428.217965  \n",
       "2959                         0.0                         0.0  30463.228496  \n",
       "2960                         0.0                         0.0  30470.538365  \n",
       "2961                         0.0                         0.0  30513.786966  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "fd6d8e1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2956</th>\n",
       "      <td>2022-10-21</td>\n",
       "      <td>19172.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>2022-10-22</td>\n",
       "      <td>19208.189453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>2022-10-23</td>\n",
       "      <td>19567.007812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>2022-10-24</td>\n",
       "      <td>19345.572266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>19484.539062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ds             y\n",
       "2956  2022-10-21  19172.468750\n",
       "2957  2022-10-22  19208.189453\n",
       "2958  2022-10-23  19567.007812\n",
       "2959  2022-10-24  19345.572266\n",
       "2960  2022-10-25  19484.539062"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a8263478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_data[['ds','y']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "6bfea5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = pd.DataFrame({'y':test_data['y'],'yhat': forecast['yhat_lower'][len(train_data)-1:-1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "77670011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2956</th>\n",
       "      <td>2022-10-21</td>\n",
       "      <td>19172.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2957</th>\n",
       "      <td>2022-10-22</td>\n",
       "      <td>19208.189453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2958</th>\n",
       "      <td>2022-10-23</td>\n",
       "      <td>19567.007812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>2022-10-24</td>\n",
       "      <td>19345.572266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>2022-10-25</td>\n",
       "      <td>19484.539062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ds             y\n",
       "2956  2022-10-21  19172.468750\n",
       "2957  2022-10-22  19208.189453\n",
       "2958  2022-10-23  19567.007812\n",
       "2959  2022-10-24  19345.572266\n",
       "2960  2022-10-25  19484.539062"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "80e53d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph['Date'] = test_data['ds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "9e317814",
   "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>y</th>\n",
       "      <th>yhat</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2931</th>\n",
       "      <td>19222.671875</td>\n",
       "      <td>22706.273575</td>\n",
       "      <td>2022-09-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2932</th>\n",
       "      <td>19110.546875</td>\n",
       "      <td>23100.609559</td>\n",
       "      <td>2022-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>19426.720703</td>\n",
       "      <td>22743.368666</td>\n",
       "      <td>2022-09-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>19573.050781</td>\n",
       "      <td>22872.501296</td>\n",
       "      <td>2022-09-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>19431.789062</td>\n",
       "      <td>22843.102904</td>\n",
       "      <td>2022-09-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 y          yhat        Date\n",
       "2931  19222.671875  22706.273575  2022-09-26\n",
       "2932  19110.546875  23100.609559  2022-09-27\n",
       "2933  19426.720703  22743.368666  2022-09-28\n",
       "2934  19573.050781  22872.501296  2022-09-29\n",
       "2935  19431.789062  22843.102904  2022-09-30"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bca98d22",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph.set_index('Date',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5c35fb81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph.plot(figsize = (20,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5d1b1f9",
   "metadata": {},
   "source": [
    "# Neural Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "028a3569",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-09-17 00:00:00+00:00</th>\n",
       "      <td>465.864014</td>\n",
       "      <td>468.174011</td>\n",
       "      <td>452.421997</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>21056800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-18 00:00:00+00:00</th>\n",
       "      <td>456.859985</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>413.104004</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>34483200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-19 00:00:00+00:00</th>\n",
       "      <td>424.102997</td>\n",
       "      <td>427.834991</td>\n",
       "      <td>384.532013</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>37919700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-20 00:00:00+00:00</th>\n",
       "      <td>394.673004</td>\n",
       "      <td>423.295990</td>\n",
       "      <td>389.882996</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>36863600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-21 00:00:00+00:00</th>\n",
       "      <td>408.084991</td>\n",
       "      <td>412.425995</td>\n",
       "      <td>393.181000</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>26580100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Date                                                                        \n",
       "2014-09-17 00:00:00+00:00  465.864014  468.174011  452.421997  457.334015   \n",
       "2014-09-18 00:00:00+00:00  456.859985  456.859985  413.104004  424.440002   \n",
       "2014-09-19 00:00:00+00:00  424.102997  427.834991  384.532013  394.795990   \n",
       "2014-09-20 00:00:00+00:00  394.673004  423.295990  389.882996  408.903992   \n",
       "2014-09-21 00:00:00+00:00  408.084991  412.425995  393.181000  398.821014   \n",
       "\n",
       "                            Adj Close    Volume  \n",
       "Date                                             \n",
       "2014-09-17 00:00:00+00:00  457.334015  21056800  \n",
       "2014-09-18 00:00:00+00:00  424.440002  34483200  \n",
       "2014-09-19 00:00:00+00:00  394.795990  37919700  \n",
       "2014-09-20 00:00:00+00:00  408.903992  36863600  \n",
       "2014-09-21 00:00:00+00:00  398.821014  26580100  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b22f1352",
   "metadata": {},
   "outputs": [],
   "source": [
    "from neuralprophet import NeuralProphet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "03cc1380",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralProphet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f3662c41",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>y</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2014-09-17</td>\n",
       "      <td>457.334015</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>424.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>394.795990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-20</td>\n",
       "      <td>408.903992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-21</td>\n",
       "      <td>398.821014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           ds           y\n",
       "0  2014-09-17  457.334015\n",
       "1  2014-09-18  424.440002\n",
       "2  2014-09-19  394.795990\n",
       "3  2014-09-20  408.903992\n",
       "4  2014-09-21  398.821014"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b77e1b49",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2931</th>\n",
       "      <td>2022-09-26</td>\n",
       "      <td>19222.671875</td>\n",
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       "    <tr>\n",
       "      <th>2932</th>\n",
       "      <td>2022-09-27</td>\n",
       "      <td>19110.546875</td>\n",
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       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>2022-09-28</td>\n",
       "      <td>19426.720703</td>\n",
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       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>2022-09-29</td>\n",
       "      <td>19573.050781</td>\n",
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       "    <tr>\n",
       "      <th>2935</th>\n",
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       "              ds             y\n",
       "2931  2022-09-26  19222.671875\n",
       "2932  2022-09-27  19110.546875\n",
       "2933  2022-09-28  19426.720703\n",
       "2934  2022-09-29  19573.050781\n",
       "2935  2022-09-30  19431.789062"
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     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "91ed7a19",
   "metadata": {},
   "outputs": [
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       "      <td>2022-09-29</td>\n",
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       "              ds             y\n",
       "2931  2022-09-26  19222.671875\n",
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       "2933  2022-09-28  19426.720703\n",
       "2934  2022-09-29  19573.050781\n",
       "2935  2022-09-30  19431.789062"
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     "execution_count": 91,
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    "test_data.head()"
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  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d2c36ed1",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['ds'] = pd.to_datetime(test_data['ds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1e0328b6",
   "metadata": {},
   "outputs": [
    {
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       "      <th>2956</th>\n",
       "      <td>2022-10-21</td>\n",
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       "      <th>2957</th>\n",
       "      <td>2022-10-22</td>\n",
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       "      <th>2958</th>\n",
       "      <td>2022-10-23</td>\n",
       "      <td>19567.007812</td>\n",
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       "    <tr>\n",
       "      <th>2959</th>\n",
       "      <td>2022-10-24</td>\n",
       "      <td>19345.572266</td>\n",
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       "    <tr>\n",
       "      <th>2960</th>\n",
       "      <td>2022-10-25</td>\n",
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      "text/plain": [
       "             ds             y\n",
       "2956 2022-10-21  19172.468750\n",
       "2957 2022-10-22  19208.189453\n",
       "2958 2022-10-23  19567.007812\n",
       "2959 2022-10-24  19345.572266\n",
       "2960 2022-10-25  19484.539062"
      ]
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     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a8228a70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.966% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.\n",
      "INFO - (NP.utils.set_auto_seasonalities) - Disabling daily seasonality. Run NeuralProphet with daily_seasonality=True to override this.\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 32\n",
      "INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 142\n"
     ]
    },
    {
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       "  0%|          | 0/137 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.utils_torch.lr_range_test) - lr-range-test results: steep: 9.03E-02, min: 1.03E+00\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.utils_torch.lr_range_test) - lr-range-test results: steep: 9.03E-02, min: 1.40E+00\n",
      "INFO - (NP.forecaster._init_train_loader) - lr-range-test selected learning rate: 9.35E-02\n",
      "Epoch[142/142]: 100%|████████████████████████████████████████████████████████████████| 142/142 [00:16<00:00,  8.44it/s]\n"
     ]
    }
   ],
   "source": [
    "model.fit(train_data, freq='D',minimal = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "e7c8d4d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 99.966% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 96.667% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils._infer_frequency) - Major frequency D corresponds to 96.667% of the data.\n",
      "INFO - (NP.df_utils._infer_frequency) - Defined frequency is equal to major frequency - D\n",
      "INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column\n"
     ]
    },
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       "      <td>48306.269531</td>\n",
       "      <td>44417.714844</td>\n",
       "      <td>3869.002197</td>\n",
       "      <td>19.550846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds     y residual1         yhat1         trend  season_yearly  \\\n",
       "25 2022-10-21  None       NaN  47752.664062  44326.289062    3407.666504   \n",
       "26 2022-10-22  None       NaN  47828.894531  44349.144531    3544.648438   \n",
       "27 2022-10-23  None       NaN  48049.582031  44372.003906    3667.337158   \n",
       "28 2022-10-24  None       NaN  48172.281250  44394.859375    3775.497314   \n",
       "29 2022-10-25  None       NaN  48306.269531  44417.714844    3869.002197   \n",
       "\n",
       "    season_weekly  \n",
       "25      18.709747  \n",
       "26     -64.896538  \n",
       "27      10.240924  \n",
       "28       1.924059  \n",
       "29      19.550846  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = model.make_future_dataframe(train_data[:-1],periods = 30)\n",
    "forecast = model.predict(future)\n",
    "forecast.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3da2df79",
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = pd.merge(test_data,forecast,on = 'ds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2ad140ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast[['y_x','yhat1']].plot(figsize = (20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58aa73c",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d528f89d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-17</td>\n",
       "      <td>457.334015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>424.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>394.795990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-20</td>\n",
       "      <td>408.903992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-21</td>\n",
       "      <td>398.821014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds           y\n",
       "0  2014-09-17  457.334015\n",
       "1  2014-09-18  424.440002\n",
       "2  2014-09-19  394.795990\n",
       "3  2014-09-20  408.903992\n",
       "4  2014-09-21  398.821014"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "271414e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2931</th>\n",
       "      <td>2022-09-26</td>\n",
       "      <td>19222.671875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2932</th>\n",
       "      <td>2022-09-27</td>\n",
       "      <td>19110.546875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>2022-09-28</td>\n",
       "      <td>19426.720703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2934</th>\n",
       "      <td>2022-09-29</td>\n",
       "      <td>19573.050781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>2022-09-30</td>\n",
       "      <td>19431.789062</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds             y\n",
       "2931 2022-09-26  19222.671875\n",
       "2932 2022-09-27  19110.546875\n",
       "2933 2022-09-28  19426.720703\n",
       "2934 2022-09-29  19573.050781\n",
       "2935 2022-09-30  19431.789062"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d31877a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2927</th>\n",
       "      <td>2022-09-22</td>\n",
       "      <td>19413.550781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2928</th>\n",
       "      <td>2022-09-23</td>\n",
       "      <td>19297.638672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>2022-09-24</td>\n",
       "      <td>18937.011719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2930</th>\n",
       "      <td>2022-09-25</td>\n",
       "      <td>18802.097656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2931</th>\n",
       "      <td>2022-09-26</td>\n",
       "      <td>19222.671875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ds             y\n",
       "2927  2022-09-22  19413.550781\n",
       "2928  2022-09-23  19297.638672\n",
       "2929  2022-09-24  18937.011719\n",
       "2930  2022-09-25  18802.097656\n",
       "2931  2022-09-26  19222.671875"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2342214",
   "metadata": {},
   "source": [
    "Before Training Model we need to reshape our data for LSTM So lets go ahead reshape our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "eb703b2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_data['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "8ae2b588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2927    19413.550781\n",
       "2928    19297.638672\n",
       "2929    18937.011719\n",
       "2930    18802.097656\n",
       "2931    19222.671875\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "85dfc425",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler \n",
    "scaled = MinMaxScaler()\n",
    "values = scaled.fit_transform(np.array(X).reshape(-1,1))\n",
    "X_train = values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e77f9d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00414359],\n",
       "       [0.00365546],\n",
       "       [0.00321557],\n",
       "       ...,\n",
       "       [0.27836865],\n",
       "       [0.27636663],\n",
       "       [0.28260764]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "221a1f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2932, 1)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c9a3052e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_data['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2049e273",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler \n",
    "scaled = MinMaxScaler()\n",
    "values = scaled.fit_transform(np.array(X_test).reshape(-1,1))\n",
    "X_test = values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "01c8bbab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 1)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee6b0f68",
   "metadata": {},
   "source": [
    "# Data Preprocessing creating timesteps = 10\n",
    "You can select timestep for as much as you like it will convert our data set in that way so basically f1..f10 and then o/p after that value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "d4db3177",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create(dataset, time_step):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-time_step-1):\n",
    "        a = dataset[i:(i+time_step), 0]  \n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + time_step, 0])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "536646ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating The New Dataset\n",
    "X_train,y_train = create(X_train,time_step = 10)\n",
    "X_test,y_test = create(X_test,time_step = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "12385d04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2921, 10) (19, 10)\n",
      "(2921,) (19,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,X_test.shape) # For each 11 time step we have a output \n",
    "print(y_train.shape,y_test.shape) # For each 11 time step we have a output "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186c0742",
   "metadata": {},
   "source": [
    "Reshaping input to be [samples, time steps, features] In this way lstm Accepts Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "54f1edad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential \n",
    "from tensorflow.keras.layers import Dense,Dropout,LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3c4df9ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
    "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b575cb18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19, 10, 1)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "79255d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally Wait Is Over Lets Start Building Model\n",
    "model = Sequential()\n",
    "model.add(LSTM(150,return_sequences=True,input_shape=(X_train.shape[1],X_train.shape[2])))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(150,return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(150))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error' , metrics = ['mse', 'mae'],optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7f7873cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 10, 150)           91200     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 10, 150)           0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 10, 150)           180600    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 10, 150)           0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 150)               180600    \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 150)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 151       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 452,551\n",
      "Trainable params: 452,551\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "fad8ba64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "46/46 [==============================] - 5s 25ms/step - loss: 0.0088 - mse: 0.0088 - mae: 0.0466\n",
      "Epoch 2/300\n",
      "46/46 [==============================] - 1s 24ms/step - loss: 0.0014 - mse: 0.0014 - mae: 0.0202\n",
      "Epoch 3/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 0.0014 - mse: 0.0014 - mae: 0.0208\n",
      "Epoch 4/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 0.0011 - mse: 0.0011 - mae: 0.0178\n",
      "Epoch 5/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 0.0011 - mse: 0.0011 - mae: 0.0181\n",
      "Epoch 6/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 0.0013 - mse: 0.0013 - mae: 0.0187\n",
      "Epoch 7/300\n",
      "46/46 [==============================] - 1s 25ms/step - loss: 0.0010 - mse: 0.0010 - mae: 0.0170\n",
      "Epoch 8/300\n",
      "46/46 [==============================] - 1s 29ms/step - loss: 0.0011 - mse: 0.0011 - mae: 0.0175\n",
      "Epoch 9/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 9.0933e-04 - mse: 9.0933e-04 - mae: 0.0158\n",
      "Epoch 10/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 9.9832e-04 - mse: 9.9832e-04 - mae: 0.0166\n",
      "Epoch 11/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 9.5289e-04 - mse: 9.5289e-04 - mae: 0.0163\n",
      "Epoch 12/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 8.5555e-04 - mse: 8.5555e-04 - mae: 0.0156\n",
      "Epoch 13/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 9.1279e-04 - mse: 9.1279e-04 - mae: 0.0161\n",
      "Epoch 14/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 0.0010 - mse: 0.0010 - mae: 0.0175\n",
      "Epoch 15/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 9.5679e-04 - mse: 9.5679e-04 - mae: 0.0164\n",
      "Epoch 16/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 8.4942e-04 - mse: 8.4942e-04 - mae: 0.0161\n",
      "Epoch 17/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 8.6835e-04 - mse: 8.6835e-04 - mae: 0.0158\n",
      "Epoch 18/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 8.3152e-04 - mse: 8.3152e-04 - mae: 0.0157\n",
      "Epoch 19/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 8.2720e-04 - mse: 8.2720e-04 - mae: 0.0154\n",
      "Epoch 20/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 6.8612e-04 - mse: 6.8612e-04 - mae: 0.0138\n",
      "Epoch 21/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 6.7771e-04 - mse: 6.7771e-04 - mae: 0.0142\n",
      "Epoch 22/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 8.3814e-04 - mse: 8.3814e-04 - mae: 0.0152\n",
      "Epoch 23/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 6.9109e-04 - mse: 6.9109e-04 - mae: 0.0142\n",
      "Epoch 24/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 6.9963e-04 - mse: 6.9963e-04 - mae: 0.0143\n",
      "Epoch 25/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 7.3746e-04 - mse: 7.3746e-04 - mae: 0.0146\n",
      "Epoch 26/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 6.3964e-04 - mse: 6.3964e-04 - mae: 0.0141\n",
      "Epoch 27/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 6.9689e-04 - mse: 6.9689e-04 - mae: 0.0144\n",
      "Epoch 28/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 6.1583e-04 - mse: 6.1583e-04 - mae: 0.0137\n",
      "Epoch 29/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 7.0702e-04 - mse: 7.0702e-04 - mae: 0.0147\n",
      "Epoch 30/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 7.1427e-04 - mse: 7.1427e-04 - mae: 0.0148\n",
      "Epoch 31/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 5.7497e-04 - mse: 5.7497e-04 - mae: 0.0133\n",
      "Epoch 32/300\n",
      "46/46 [==============================] - 1s 27ms/step - loss: 7.2423e-04 - mse: 7.2423e-04 - mae: 0.0145\n",
      "Epoch 33/300\n",
      "46/46 [==============================] - 1s 28ms/step - loss: 6.1702e-04 - mse: 6.1702e-04 - mae: 0.0137\n",
      "Epoch 34/300\n",
      "46/46 [==============================] - 1s 31ms/step - loss: 6.1489e-04 - mse: 6.1489e-04 - mae: 0.0141\n",
      "Epoch 35/300\n",
      "46/46 [==============================] - 1s 24ms/step - loss: 0.0011 - mse: 0.0011 - mae: 0.0180\n",
      "Epoch 36/300\n",
      "46/46 [==============================] - 1s 24ms/step - loss: 6.1202e-04 - mse: 6.1202e-04 - mae: 0.0147\n",
      "Epoch 37/300\n",
      "46/46 [==============================] - 1s 24ms/step - loss: 6.1103e-04 - mse: 6.1103e-04 - mae: 0.0143\n",
      "Epoch 38/300\n",
      "46/46 [==============================] - 1s 24ms/step - loss: 5.3369e-04 - mse: 5.3369e-04 - mae: 0.0129\n",
      "Epoch 39/300\n",
      "46/46 [==============================] - 1s 24ms/step - loss: 5.9845e-04 - mse: 5.9845e-04 - mae: 0.0137\n",
      "Epoch 40/300\n",
      "46/46 [==============================] - 1s 24ms/step - loss: 5.6506e-04 - mse: 5.6506e-04 - mae: 0.0133\n",
      "Epoch 41/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 5.1586e-04 - mse: 5.1586e-04 - mae: 0.0128\n",
      "Epoch 42/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 6.0770e-04 - mse: 6.0770e-04 - mae: 0.0140\n",
      "Epoch 43/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 5.9044e-04 - mse: 5.9044e-04 - mae: 0.0139\n",
      "Epoch 44/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 4.8696e-04 - mse: 4.8696e-04 - mae: 0.0126\n",
      "Epoch 45/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 5.5857e-04 - mse: 5.5857e-04 - mae: 0.0137\n",
      "Epoch 46/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.9097e-04 - mse: 4.9097e-04 - mae: 0.0126\n",
      "Epoch 47/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 5.2680e-04 - mse: 5.2680e-04 - mae: 0.0133\n",
      "Epoch 48/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 6.2294e-04 - mse: 6.2294e-04 - mae: 0.0143\n",
      "Epoch 49/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 6.1114e-04 - mse: 6.1114e-04 - mae: 0.0141\n",
      "Epoch 50/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 5.1756e-04 - mse: 5.1756e-04 - mae: 0.0133\n",
      "Epoch 51/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.7594e-04 - mse: 4.7594e-04 - mae: 0.0125\n",
      "Epoch 52/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 6.0386e-04 - mse: 6.0386e-04 - mae: 0.0141\n",
      "Epoch 53/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.8296e-04 - mse: 4.8296e-04 - mae: 0.0132\n",
      "Epoch 54/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.5468e-04 - mse: 4.5468e-04 - mae: 0.0123\n",
      "Epoch 55/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.9012e-04 - mse: 4.9012e-04 - mae: 0.0129\n",
      "Epoch 56/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 4.7166e-04 - mse: 4.7166e-04 - mae: 0.0124\n",
      "Epoch 57/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.4505e-04 - mse: 4.4505e-04 - mae: 0.0130\n",
      "Epoch 58/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.9833e-04 - mse: 4.9833e-04 - mae: 0.0131\n",
      "Epoch 59/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.6891e-04 - mse: 4.6891e-04 - mae: 0.0131\n",
      "Epoch 60/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 5.2824e-04 - mse: 5.2824e-04 - mae: 0.0137\n",
      "Epoch 61/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.8144e-04 - mse: 4.8144e-04 - mae: 0.0129\n",
      "Epoch 62/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 5.0554e-04 - mse: 5.0554e-04 - mae: 0.0133\n",
      "Epoch 63/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 5.1041e-04 - mse: 5.1041e-04 - mae: 0.0131\n",
      "Epoch 64/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.6337e-04 - mse: 4.6337e-04 - mae: 0.0136\n",
      "Epoch 65/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 4.3412e-04 - mse: 4.3412e-04 - mae: 0.0123\n",
      "Epoch 66/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.4721e-04 - mse: 4.4721e-04 - mae: 0.0124\n",
      "Epoch 67/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.4402e-04 - mse: 4.4402e-04 - mae: 0.0128\n",
      "Epoch 68/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.7089e-04 - mse: 4.7089e-04 - mae: 0.0129\n",
      "Epoch 69/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 5.5138e-04 - mse: 5.5138e-04 - mae: 0.0138\n",
      "Epoch 70/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 4.6053e-04 - mse: 4.6053e-04 - mae: 0.0131\n",
      "Epoch 71/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46/46 [==============================] - 1s 23ms/step - loss: 4.6701e-04 - mse: 4.6701e-04 - mae: 0.0129\n",
      "Epoch 72/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.6490e-04 - mse: 4.6490e-04 - mae: 0.0129\n",
      "Epoch 73/300\n",
      "46/46 [==============================] - 1s 23ms/step - loss: 4.9512e-04 - mse: 4.9512e-04 - mae: 0.0133\n",
      "Epoch 74/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 5.5787e-04 - mse: 5.5787e-04 - mae: 0.0141\n",
      "Epoch 75/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.8463e-04 - mse: 4.8463e-04 - mae: 0.0133\n",
      "Epoch 76/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.8764e-04 - mse: 4.8764e-04 - mae: 0.0132\n",
      "Epoch 77/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.3427e-04 - mse: 4.3427e-04 - mae: 0.0123\n",
      "Epoch 78/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 5.2022e-04 - mse: 5.2022e-04 - mae: 0.0135\n",
      "Epoch 79/300\n",
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    },
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      "Epoch 219/300\n",
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      "Epoch 220/300\n",
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      "Epoch 221/300\n",
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      "Epoch 229/300\n",
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      "Epoch 231/300\n",
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      "Epoch 233/300\n",
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      "Epoch 234/300\n",
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      "Epoch 236/300\n",
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      "Epoch 237/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.0059e-04 - mse: 4.0059e-04 - mae: 0.0127\n",
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      "Epoch 239/300\n",
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      "Epoch 240/300\n",
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      "Epoch 241/300\n",
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      "Epoch 243/300\n",
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      "Epoch 244/300\n",
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      "Epoch 245/300\n",
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      "Epoch 246/300\n",
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      "Epoch 247/300\n",
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      "Epoch 248/300\n",
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      "Epoch 249/300\n",
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      "Epoch 250/300\n",
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      "Epoch 251/300\n",
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      "Epoch 252/300\n",
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      "Epoch 254/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 3.5354e-04 - mse: 3.5354e-04 - mae: 0.0117\n",
      "Epoch 255/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 3.4893e-04 - mse: 3.4893e-04 - mae: 0.0116\n",
      "Epoch 256/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 3.5031e-04 - mse: 3.5031e-04 - mae: 0.0116\n",
      "Epoch 257/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 3.4180e-04 - mse: 3.4180e-04 - mae: 0.0114\n",
      "Epoch 258/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 3.3713e-04 - mse: 3.3713e-04 - mae: 0.0114\n",
      "Epoch 259/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.2512e-04 - mse: 3.2512e-04 - mae: 0.0116\n",
      "Epoch 260/300\n",
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      "Epoch 261/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 4.6454e-04 - mse: 4.6454e-04 - mae: 0.0131\n",
      "Epoch 262/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.5395e-04 - mse: 4.5395e-04 - mae: 0.0133\n",
      "Epoch 263/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5849e-04 - mse: 3.5849e-04 - mae: 0.0117\n",
      "Epoch 264/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.3250e-04 - mse: 3.3250e-04 - mae: 0.0114\n",
      "Epoch 265/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5339e-04 - mse: 3.5339e-04 - mae: 0.0115\n",
      "Epoch 266/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.6199e-04 - mse: 3.6199e-04 - mae: 0.0117\n",
      "Epoch 267/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.1997e-04 - mse: 3.1997e-04 - mae: 0.0113\n",
      "Epoch 268/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.1252e-04 - mse: 3.1252e-04 - mae: 0.0113\n",
      "Epoch 269/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.2978e-04 - mse: 3.2978e-04 - mae: 0.0113\n",
      "Epoch 270/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.6545e-04 - mse: 3.6545e-04 - mae: 0.0123\n",
      "Epoch 271/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.4984e-04 - mse: 3.4984e-04 - mae: 0.0117\n",
      "Epoch 272/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5268e-04 - mse: 3.5268e-04 - mae: 0.0119\n",
      "Epoch 273/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5758e-04 - mse: 3.5758e-04 - mae: 0.0117\n",
      "Epoch 274/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 3.5799e-04 - mse: 3.5799e-04 - mae: 0.0116\n",
      "Epoch 275/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.1577e-04 - mse: 3.1577e-04 - mae: 0.0109\n",
      "Epoch 276/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.8047e-04 - mse: 3.8047e-04 - mae: 0.0119\n",
      "Epoch 277/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46/46 [==============================] - 1s 22ms/step - loss: 3.1723e-04 - mse: 3.1723e-04 - mae: 0.0109\n",
      "Epoch 278/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 3.5744e-04 - mse: 3.5744e-04 - mae: 0.0118\n",
      "Epoch 279/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5579e-04 - mse: 3.5579e-04 - mae: 0.0116\n",
      "Epoch 280/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.7318e-04 - mse: 3.7318e-04 - mae: 0.0119\n",
      "Epoch 281/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.6969e-04 - mse: 3.6969e-04 - mae: 0.0121\n",
      "Epoch 282/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.0667e-04 - mse: 3.0667e-04 - mae: 0.0109\n",
      "Epoch 283/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5693e-04 - mse: 3.5693e-04 - mae: 0.0118\n",
      "Epoch 284/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.4602e-04 - mse: 3.4602e-04 - mae: 0.0116\n",
      "Epoch 285/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 4.0015e-04 - mse: 4.0015e-04 - mae: 0.0124\n",
      "Epoch 286/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.4986e-04 - mse: 3.4986e-04 - mae: 0.0115\n",
      "Epoch 287/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5410e-04 - mse: 3.5410e-04 - mae: 0.0116\n",
      "Epoch 288/300\n",
      "46/46 [==============================] - 1s 25ms/step - loss: 3.4066e-04 - mse: 3.4066e-04 - mae: 0.0115\n",
      "Epoch 289/300\n",
      "46/46 [==============================] - 1s 25ms/step - loss: 3.8932e-04 - mse: 3.8932e-04 - mae: 0.0124\n",
      "Epoch 290/300\n",
      "46/46 [==============================] - 1s 25ms/step - loss: 3.3834e-04 - mse: 3.3834e-04 - mae: 0.0113\n",
      "Epoch 291/300\n",
      "46/46 [==============================] - 1s 25ms/step - loss: 3.5266e-04 - mse: 3.5266e-04 - mae: 0.0117\n",
      "Epoch 292/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.1636e-04 - mse: 3.1636e-04 - mae: 0.0109\n",
      "Epoch 293/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5560e-04 - mse: 3.5560e-04 - mae: 0.0117\n",
      "Epoch 294/300\n",
      "46/46 [==============================] - 1s 21ms/step - loss: 3.3754e-04 - mse: 3.3754e-04 - mae: 0.0114\n",
      "Epoch 295/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.2856e-04 - mse: 3.2856e-04 - mae: 0.0115\n",
      "Epoch 296/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.5814e-04 - mse: 3.5814e-04 - mae: 0.0117\n",
      "Epoch 297/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.4377e-04 - mse: 3.4377e-04 - mae: 0.0116\n",
      "Epoch 298/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.8398e-04 - mse: 3.8398e-04 - mae: 0.0125\n",
      "Epoch 299/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.3102e-04 - mse: 3.3102e-04 - mae: 0.0112\n",
      "Epoch 300/300\n",
      "46/46 [==============================] - 1s 22ms/step - loss: 3.3353e-04 - mse: 3.3353e-04 - mae: 0.0112\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b94d9adaf0>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,epochs=300,batch_size=64,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "448d654d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "       [[0.08767286],\n",
       "        [0.26207732],\n",
       "        [0.10949551],\n",
       "        [0.01819965],\n",
       "        [0.17326528],\n",
       "        [0.3919209 ],\n",
       "        [0.22456907],\n",
       "        [0.07381841],\n",
       "        [0.00745149],\n",
       "        [0.09929428]],\n",
       "\n",
       "       [[0.26207732],\n",
       "        [0.10949551],\n",
       "        [0.01819965],\n",
       "        [0.17326528],\n",
       "        [0.3919209 ],\n",
       "        [0.22456907],\n",
       "        [0.07381841],\n",
       "        [0.00745149],\n",
       "        [0.09929428],\n",
       "        [0.12692614]],\n",
       "\n",
       "       [[0.10949551],\n",
       "        [0.01819965],\n",
       "        [0.17326528],\n",
       "        [0.3919209 ],\n",
       "        [0.22456907],\n",
       "        [0.07381841],\n",
       "        [0.00745149],\n",
       "        [0.09929428],\n",
       "        [0.12692614],\n",
       "        [0.40449114]]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "b33a7ab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 684ms/step\n"
     ]
    }
   ],
   "source": [
    "y_hat = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a00a5486",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat = y_hat.reshape(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "dcadfe1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "checking_df = pd.DataFrame({'yhat':y_hat,'y':y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "aadd0744",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "checking_df.plot(figsize = (20,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "098eebf1",
   "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>yhat</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.668656</td>\n",
       "      <td>0.704967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.709264</td>\n",
       "      <td>0.388898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.447670</td>\n",
       "      <td>0.288118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.332168</td>\n",
       "      <td>0.311215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.286316</td>\n",
       "      <td>0.075326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.131503</td>\n",
       "      <td>0.005655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.038818</td>\n",
       "      <td>0.087673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.110768</td>\n",
       "      <td>0.262077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.334238</td>\n",
       "      <td>0.109496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.181342</td>\n",
       "      <td>0.018200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.034089</td>\n",
       "      <td>0.173265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.127941</td>\n",
       "      <td>0.391921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.442231</td>\n",
       "      <td>0.224569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.264520</td>\n",
       "      <td>0.073818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.099454</td>\n",
       "      <td>0.007451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.001217</td>\n",
       "      <td>0.099294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.079228</td>\n",
       "      <td>0.126926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.175999</td>\n",
       "      <td>0.404491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.470716</td>\n",
       "      <td>0.233199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        yhat         y\n",
       "0   0.668656  0.704967\n",
       "1   0.709264  0.388898\n",
       "2   0.447670  0.288118\n",
       "3   0.332168  0.311215\n",
       "4   0.286316  0.075326\n",
       "5   0.131503  0.005655\n",
       "6   0.038818  0.087673\n",
       "7   0.110768  0.262077\n",
       "8   0.334238  0.109496\n",
       "9   0.181342  0.018200\n",
       "10  0.034089  0.173265\n",
       "11  0.127941  0.391921\n",
       "12  0.442231  0.224569\n",
       "13  0.264520  0.073818\n",
       "14  0.099454  0.007451\n",
       "15 -0.001217  0.099294\n",
       "16  0.079228  0.126926\n",
       "17  0.175999  0.404491\n",
       "18  0.470716  0.233199"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checking_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "591aa9d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "checking_df['ylag']= checking_df['yhat'].shift(-1)"
   ]
  },
  {
   "attachments": {
    "download.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "509c9b39",
   "metadata": {},
   "source": [
    "# Few Days Back When i re runed the script the data that we got From LSTM was\n",
    "![download.png](attachment:download.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "1d805828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Recent Data also show same characterstic by the LSTM Model.\n",
    "checking_df.plot(figsize = (20,9)) #don't know why but its predicting the trajectory just lagging in terms of days so"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aaeec6a",
   "metadata": {},
   "source": [
    "# My final will be A Transformer- Temporal Fusion Transformer \n",
    "After this we will start building the complete end to end deployment of one model whichever works the best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "2a7c2aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-17</td>\n",
       "      <td>457.334015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>424.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>394.795990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-20</td>\n",
       "      <td>408.903992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-21</td>\n",
       "      <td>398.821014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds           y\n",
       "0  2014-09-17  457.334015\n",
       "1  2014-09-18  424.440002\n",
       "2  2014-09-19  394.795990\n",
       "3  2014-09-20  408.903992\n",
       "4  2014-09-21  398.821014"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3deae135",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2921, 10, 1)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "87f812eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from darts import TimeSeries\n",
    "from darts.models.forecasting.tft_model import TFTModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "0f0b31b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from darts.dataprocessing.transformers.static_covariates_transformer import StaticCovariatesTransformer\n",
    "temporalFTmodel = TFTModel(input_chunk_length = 2001 ,output_chunk_length = 80,n_epochs = 50,add_relative_index = True,dropout=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "049ff9fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler \n",
    "scaled = MinMaxScaler()\n",
    "values = scaled.fit_transform(np.array(train_data['y']).reshape(-1,1))\n",
    "train = values\n",
    "# Test Data\n",
    "scaled = MinMaxScaler()\n",
    "values = scaled.fit_transform(np.array(test_data['y']).reshape(-1,1))\n",
    "test = values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "e0fde494",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_training = TimeSeries.from_series(train)\n",
    "data_val = TimeSeries.from_series(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "f79f237a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;TimeSeries (DataArray) (time: 5, component: 1, sample: 1)&gt;\n",
       "array([[[0.00414359]],\n",
       "\n",
       "       [[0.00365546]],\n",
       "\n",
       "       [[0.00321557]],\n",
       "\n",
       "       [[0.00342492]],\n",
       "\n",
       "       [[0.0032753 ]]])\n",
       "Coordinates:\n",
       "  * time       (time) int64 0 1 2 3 4\n",
       "  * component  (component) &lt;U1 &#x27;0&#x27;\n",
       "Dimensions without coordinates: sample\n",
       "Attributes:\n",
       "    static_covariates:  None\n",
       "    hierarchy:          None</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>TimeSeries (DataArray)</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 5</li><li><span class='xr-has-index'>component</span>: 1</li><li><span>sample</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d32808f6-0164-4413-ac24-4f4f4c481e35' class='xr-array-in' type='checkbox' checked><label for='section-d32808f6-0164-4413-ac24-4f4f4c481e35' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.004144 0.003655 0.003216 0.003425 0.003275</span></div><div class='xr-array-data'><pre>array([[[0.00414359]],\n",
       "\n",
       "       [[0.00365546]],\n",
       "\n",
       "       [[0.00321557]],\n",
       "\n",
       "       [[0.00342492]],\n",
       "\n",
       "       [[0.0032753 ]]])</pre></div></div></li><li class='xr-section-item'><input id='section-d2f8ff47-e8a4-4fdd-b6a1-8aba6ace015f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2f8ff47-e8a4-4fdd-b6a1-8aba6ace015f' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-38cc0431-7c12-4a20-b3de-7b4e6e29d950' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38cc0431-7c12-4a20-b3de-7b4e6e29d950' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c2e1363-fef8-4b33-8e6c-ec5800918282' class='xr-var-data-in' type='checkbox'><label for='data-8c2e1363-fef8-4b33-8e6c-ec5800918282' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>component</span></div><div class='xr-var-dims'>(component)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27;</div><input id='attrs-d0c9c3b8-764c-4d0e-9b19-d429b6b1ca6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0c9c3b8-764c-4d0e-9b19-d429b6b1ca6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebd86d7f-46ac-4e07-a39d-e344e38add72' class='xr-var-data-in' type='checkbox'><label for='data-ebd86d7f-46ac-4e07-a39d-e344e38add72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;0&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e2a4457-ffa6-429e-b568-480ed958489b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e2a4457-ffa6-429e-b568-480ed958489b' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>static_covariates :</span></dt><dd>None</dd><dt><span>hierarchy :</span></dt><dd>None</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<TimeSeries (DataArray) (time: 5, component: 1, sample: 1)>\n",
       "array([[[0.00414359]],\n",
       "\n",
       "       [[0.00365546]],\n",
       "\n",
       "       [[0.00321557]],\n",
       "\n",
       "       [[0.00342492]],\n",
       "\n",
       "       [[0.0032753 ]]])\n",
       "Coordinates:\n",
       "  * time       (time) int64 0 1 2 3 4\n",
       "  * component  (component) <U1 '0'\n",
       "Dimensions without coordinates: sample\n",
       "Attributes:\n",
       "    static_covariates:  None\n",
       "    hierarchy:          None"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_training.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "59408dbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-25 19:57:03 pytorch_lightning.utilities.rank_zero INFO: GPU available: False, used: False\n",
      "2022-10-25 19:57:03 pytorch_lightning.utilities.rank_zero INFO: TPU available: False, using: 0 TPU cores\n",
      "2022-10-25 19:57:03 pytorch_lightning.utilities.rank_zero INFO: IPU available: False, using: 0 IPUs\n",
      "2022-10-25 19:57:03 pytorch_lightning.utilities.rank_zero INFO: HPU available: False, using: 0 HPUs\n",
      "2022-10-25 19:57:03 pytorch_lightning.callbacks.model_summary INFO: \n",
      "   | Name                              | Type                             | Params\n",
      "----------------------------------------------------------------------------------------\n",
      "0  | train_metrics                     | MetricCollection                 | 0     \n",
      "1  | val_metrics                       | MetricCollection                 | 0     \n",
      "2  | input_embeddings                  | _MultiEmbedding                  | 0     \n",
      "3  | static_covariates_vsn             | _VariableSelectionNetwork        | 0     \n",
      "4  | encoder_vsn                       | _VariableSelectionNetwork        | 1.2 K \n",
      "5  | decoder_vsn                       | _VariableSelectionNetwork        | 528   \n",
      "6  | static_context_grn                | _GatedResidualNetwork            | 1.1 K \n",
      "7  | static_context_hidden_encoder_grn | _GatedResidualNetwork            | 1.1 K \n",
      "8  | static_context_cell_encoder_grn   | _GatedResidualNetwork            | 1.1 K \n",
      "9  | static_context_enrichment         | _GatedResidualNetwork            | 1.1 K \n",
      "10 | lstm_encoder                      | LSTM                             | 2.2 K \n",
      "11 | lstm_decoder                      | LSTM                             | 2.2 K \n",
      "12 | post_lstm_gan                     | _GateAddNorm                     | 576   \n",
      "13 | static_enrichment_grn             | _GatedResidualNetwork            | 1.4 K \n",
      "14 | multihead_attn                    | _InterpretableMultiHeadAttention | 676   \n",
      "15 | post_attn_gan                     | _GateAddNorm                     | 576   \n",
      "16 | feed_forward_block                | _GatedResidualNetwork            | 1.1 K \n",
      "17 | pre_output_gan                    | _GateAddNorm                     | 576   \n",
      "18 | output_layer                      | Linear                           | 289   \n",
      "----------------------------------------------------------------------------------------\n",
      "15.7 K    Trainable params\n",
      "0         Non-trainable params\n",
      "15.7 K    Total params\n",
      "0.125     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4af1004d4f614c8caf747faad50a1735",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-10-25 20:34:00 pytorch_lightning.utilities.rank_zero INFO: `Trainer.fit` stopped: `max_epochs=50` reached.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<darts.models.forecasting.tft_model.TFTModel at 0x1b95d0fda00>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temporalFTmodel.fit(data_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "fc5bc690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2649b049a41497f9b30d377d7466085",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 27it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_new = temporalFTmodel.predict(n=30,series = data_training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ab37ad86",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat_new = np.array(y_hat_new.pd_series())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "8cde3d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "tft_combine_df = pd.DataFrame({'y_hat':y_hat_new,'y_actual': np.array(data_val.pd_series())})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "136470b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_hat</th>\n",
       "      <th>y_actual</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>29</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       y_hat  y_actual\n",
       "0   0.269265  0.138129\n",
       "1   0.236574  0.051394\n",
       "2   0.209938  0.295972\n",
       "3   0.269168  0.409166\n",
       "4   0.241474  0.299892\n",
       "5   0.275717  0.207303\n",
       "6   0.277900  0.000000\n",
       "7   0.302723  0.448253\n",
       "8   0.273588  1.000000\n",
       "9   0.272236  0.863756\n",
       "10  0.336151  0.704967\n",
       "11  0.295365  0.388898\n",
       "12  0.263089  0.288118\n",
       "13  0.339987  0.311215\n",
       "14  0.285048  0.075326\n",
       "15  0.359158  0.005655\n",
       "16  0.470890  0.087673\n",
       "17  0.192438  0.262077\n",
       "18  0.252478  0.109496\n",
       "19  0.217675  0.018200\n",
       "20  0.233600  0.173265\n",
       "21  0.273476  0.391921\n",
       "22  0.259027  0.224569\n",
       "23  0.156709  0.073818\n",
       "24  0.283319  0.007451\n",
       "25  0.323032  0.099294\n",
       "26  0.326462  0.126926\n",
       "27  0.222648  0.404491\n",
       "28  0.279097  0.233199\n",
       "29  0.325306  0.340697"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tft_combine_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1ab37d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tft_combine_df.plot(figsize = (20,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae703995",
   "metadata": {},
   "source": [
    "# For now i'll be using LSTM To build END to END Deployed Version \n",
    "As for me lstm was very close if we reduce the lag its almost close so let's try to deploy it and use realtime bitcoin price to predict outcome in future i will be doing multivariate in which we will add sentiment and inflation factor in our data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36035c4f",
   "metadata": {},
   "source": [
    "# Saving Model(For Testing)\n",
    "I will create a build model python script that will re run automatically after 10 days or maybe month not decided yet this will make model continous and autonomously integrated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "ab791742",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('LSTM_build.h5',save_format = 'h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "9782125f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x1b95d84cf70>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.models.load_model('LSTM_build.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4d32e160",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_time_str = '18/09/19'\n",
    "date_time_obj1 = datetime.strptime(date_time_str, '%d/%m/%y')\n",
    "date_time_str = '23/09/29'\n",
    "date_time_obj1 = datetime.strptime(date_time_str, '%d/%m/%y')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdd8255e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}