Stock_prediction_using_ARIMA&SARIMAX.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('D:\\ML_workspace\\stock_LSTM_ARIMA\\datasets\\perrin-freres-monthly-champagne-.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01</td>\n",
       "      <td>2815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1964-02</td>\n",
       "      <td>2672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1964-03</td>\n",
       "      <td>2755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964-04</td>\n",
       "      <td>2721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964-05</td>\n",
       "      <td>2946.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Perrin Freres monthly champagne sales millions ?64-?72\n",
       "0  1964-01                                             2815.0     \n",
       "1  1964-02                                             2672.0     \n",
       "2  1964-03                                             2755.0     \n",
       "3  1964-04                                             2721.0     \n",
       "4  1964-05                                             2946.0     "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Month</th>\n",
       "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1972-07</td>\n",
       "      <td>4298.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1972-08</td>\n",
       "      <td>1413.0</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1972-09</td>\n",
       "      <td>5877.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Perrin Freres monthly champagne sales millions...</td>\n",
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      "text/plain": [
       "                                                 Month  \\\n",
       "102                                            1972-07   \n",
       "103                                            1972-08   \n",
       "104                                            1972-09   \n",
       "105                                                NaN   \n",
       "106  Perrin Freres monthly champagne sales millions...   \n",
       "\n",
       "     Perrin Freres monthly champagne sales millions ?64-?72  \n",
       "102                                             4298.0       \n",
       "103                                             1413.0       \n",
       "104                                             5877.0       \n",
       "105                                                NaN       \n",
       "106                                                NaN       "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1972-07</td>\n",
       "      <td>4298.0</td>\n",
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       "      <th>103</th>\n",
       "      <td>1972-08</td>\n",
       "      <td>1413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1972-09</td>\n",
       "      <td>5877.0</td>\n",
       "    </tr>\n",
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       "      <th>105</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Perrin Freres monthly champagne sales millions...</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                                                 Month  \\\n",
       "102                                            1972-07   \n",
       "103                                            1972-08   \n",
       "104                                            1972-09   \n",
       "105                                                NaN   \n",
       "106  Perrin Freres monthly champagne sales millions...   \n",
       "\n",
       "     Perrin Freres monthly champagne sales millions ?64-?72  \n",
       "102                                             4298.0       \n",
       "103                                             1413.0       \n",
       "104                                             5877.0       \n",
       "105                                                NaN       \n",
       "106                                                NaN       "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Month', 'Perrin Freres monthly champagne sales millions ?64-?72'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01</td>\n",
       "      <td>2815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1964-02</td>\n",
       "      <td>2672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1964-03</td>\n",
       "      <td>2755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964-04</td>\n",
       "      <td>2721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964-05</td>\n",
       "      <td>2946.0</td>\n",
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      ],
      "text/plain": [
       "     Month   sales\n",
       "0  1964-01  2815.0\n",
       "1  1964-02  2672.0\n",
       "2  1964-03  2755.0\n",
       "3  1964-04  2721.0\n",
       "4  1964-05  2946.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['Month', 'sales']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(df)):\n",
    "    if df['Month'].isnull().sum() >= 1:\n",
    "        df.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1972-05</td>\n",
       "      <td>4618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1972-06</td>\n",
       "      <td>5312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1972-07</td>\n",
       "      <td>4298.0</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1972-08</td>\n",
       "      <td>1413.0</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>1972-09</td>\n",
       "      <td>5877.0</td>\n",
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      ],
      "text/plain": [
       "       Month   sales\n",
       "100  1972-05  4618.0\n",
       "101  1972-06  5312.0\n",
       "102  1972-07  4298.0\n",
       "103  1972-08  1413.0\n",
       "104  1972-09  5877.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1964-01</td>\n",
       "      <td>2815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1964-02</td>\n",
       "      <td>2672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1964-03</td>\n",
       "      <td>2755.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1964-04</td>\n",
       "      <td>2721.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964-05</td>\n",
       "      <td>2946.0</td>\n",
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      ],
      "text/plain": [
       "     Month   sales\n",
       "0  1964-01  2815.0\n",
       "1  1964-02  2672.0\n",
       "2  1964-03  2755.0\n",
       "3  1964-04  2721.0\n",
       "4  1964-05  2946.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Month'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = pd.to_datetime(df['Month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['Month'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
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       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>2815.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-02-01</th>\n",
       "      <td>2672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-03-01</th>\n",
       "      <td>2755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-01</th>\n",
       "      <td>2721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-05-01</th>\n",
       "      <td>2946.0</td>\n",
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      ],
      "text/plain": [
       "             sales\n",
       "Month             \n",
       "1964-01-01  2815.0\n",
       "1964-02-01  2672.0\n",
       "1964-03-01  2755.0\n",
       "1964-04-01  2721.0\n",
       "1964-05-01  2946.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Month', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>105.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4761.152381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2553.502601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3113.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4217.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5221.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13916.000000</td>\n",
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      ],
      "text/plain": [
       "              sales\n",
       "count    105.000000\n",
       "mean    4761.152381\n",
       "std     2553.502601\n",
       "min     1413.000000\n",
       "25%     3113.000000\n",
       "50%     4217.000000\n",
       "75%     5221.000000\n",
       "max    13916.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 17,
     "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.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2815.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sales'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# applying adfuller (used for checking if stationary or not)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.8335930563276297,\n",
       " 0.3639157716602417,\n",
       " 11,\n",
       " 93,\n",
       " {'1%': -3.502704609582561,\n",
       "  '5%': -2.8931578098779522,\n",
       "  '10%': -2.583636712914788},\n",
       " 1478.4633060594724)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_result=adfuller(df['sales'])\n",
    "test_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# making our own adfuller, with a proper output from a function.\n",
    "#Ho: It is non stationary\n",
    "#H1: It is stationary\n",
    "\n",
    "def adfuller_test(sales):\n",
    "    result=adfuller(sales)\n",
    "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
    "    for value,label in zip(result,labels):\n",
    "        print(label+' : '+str(value) )\n",
    "    if result[1] <= 0.05: #[1] meaning p-value\n",
    "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -1.8335930563276297\n",
      "p-value : 0.3639157716602417\n",
      "#Lags Used : 11\n",
      "Number of Observations Used : 93\n",
      "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(adfuller_test(df['sales']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting to static from non static, by applying differencing method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we are shifting by 12 because our dataset for sales looks seasonal and in a year we have 12 months.\n",
    "# so insted of shifting by 1, we shift by 12.\n",
    "# we store it a new column named -- Seasonal First Difference after shifting the values.\n",
    "df['Seasonal First Difference'] = df['sales'] - df['sales'].shift(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1964-01-01       NaN\n",
       "1964-02-01       NaN\n",
       "1964-03-01       NaN\n",
       "1964-04-01       NaN\n",
       "1964-05-01       NaN\n",
       "1964-06-01       NaN\n",
       "1964-07-01       NaN\n",
       "1964-08-01       NaN\n",
       "1964-09-01       NaN\n",
       "1964-10-01       NaN\n",
       "1964-11-01       NaN\n",
       "1964-12-01       NaN\n",
       "1965-01-01    -274.0\n",
       "1965-02-01    -197.0\n",
       "1965-03-01     276.0\n",
       "1965-04-01     545.0\n",
       "1965-05-01     830.0\n",
       "1965-06-01     194.0\n",
       "1965-07-01     746.0\n",
       "1965-08-01    -453.0\n",
       "1965-09-01     673.0\n",
       "1965-10-01     173.0\n",
       "1965-11-01    1074.0\n",
       "1965-12-01    1045.0\n",
       "1966-01-01     572.0\n",
       "1966-02-01     531.0\n",
       "1966-03-01    1016.0\n",
       "1966-04-01     257.0\n",
       "1966-05-01     161.0\n",
       "1966-06-01     756.0\n",
       "1966-07-01     232.0\n",
       "1966-08-01    -186.0\n",
       "1966-09-01     -67.0\n",
       "1966-10-01     737.0\n",
       "1966-11-01     776.0\n",
       "1966-12-01     897.0\n",
       "1967-01-01    2262.0\n",
       "1967-02-01      82.0\n",
       "1967-03-01    -329.0\n",
       "1967-04-01     991.0\n",
       "Name: Seasonal First Difference, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Seasonal First Difference'].head(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -7.626619157213163\n",
      "p-value : 2.060579696813685e-11\n",
      "#Lags Used : 0\n",
      "Number of Observations Used : 92\n",
      "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "adfuller_test(df['Seasonal First Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "## therefore we converted our data into static nd stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1964-01-01      NaN\n",
       "1964-02-01      NaN\n",
       "1964-03-01      NaN\n",
       "1964-04-01      NaN\n",
       "1964-05-01      NaN\n",
       "              ...  \n",
       "1972-05-01   -392.0\n",
       "1972-06-01    438.0\n",
       "1972-07-01   -335.0\n",
       "1972-08-01   -246.0\n",
       "1972-09-01    -74.0\n",
       "Name: Seasonal First Difference, Length: 105, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Seasonal First Difference']\n",
    "\n",
    "# if the p value would have been exactly 0 , it would have been really awesome, but its still very close to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 28,
     "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['sales'].plot()\n",
    "# "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CorrelationPlot ( used for knowing how many previous data we need)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import autocorrelation_plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Lag', ylabel='Autocorrelation'>"
      ]
     },
     "execution_count": 30,
     "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": [
    "autocorrelation_plot(df['sales'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = plot_acf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = plot_pacf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from the above 2 plots we decide the values for p,d, and q \n",
    "\n",
    "# p = 1  ( AR value -- see the pacf plot -- At 1 the plot SUDDEN SHUTS OFF or the values are close to 0)\n",
    "# d = 1 (because we shifted 12 times just once)\n",
    "# q = (moving average value ---- in acf we will have exponential decrease TO 0 or 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now we implement the ARIMA model by putting in p,d,q values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As our data was seasonal, this arima model will not give a good prediction.....hence we will use SARIMA for better prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anshul Singh\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:159: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\Anshul Singh\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:159: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    }
   ],
   "source": [
    "model = ARIMA(df['sales'],order = (1,1,1))\n",
    "model_fit_arima = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['forecast'] = model_fit_arima.predict(start=90,end=103,dynamic=True)\n",
    "df[['sales','forecast']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# the above prediction is really terrible, so now we use SARIMA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Anshul Singh\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:159: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  warnings.warn('No frequency information was'\n",
      "C:\\Users\\Anshul Singh\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:159: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  warnings.warn('No frequency information was'\n"
     ]
    }
   ],
   "source": [
    "model2 = SARIMAX(df['sales'],order=(1,1,1),seasonal_order= (1,1,1,12))\n",
    "model_fit_sarimax = model2.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['forecast_sarimax'] = model_fit_sarimax.predict(start=90,end=103,dynamic = True)\n",
    "df[['sales','forecast_sarimax']].plot(figsize=(12,8))\n",
    "# we are plotting over the actual plot just to chefck if everything is working fine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from the above plot we can see that the predictions come out to be almost perfect or better than the arima model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Future Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import DateOffset\n",
    "future_dates = [df.index[-1] + DateOffset(months=x)for x in range(0,24)]\n",
    "\n",
    "# making future dates for 24 months.\n",
    "\n",
    "# ts = pd.Timestamp('2017-01-01 09:10:11')\n",
    "# ts + DateOffset(months=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('1972-09-01 00:00:00')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>sales</th>\n",
       "      <th>Seasonal First Difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>forecast_sarimax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1972-05-01</th>\n",
       "      <td>4618.0</td>\n",
       "      <td>-392.0</td>\n",
       "      <td>22.892883</td>\n",
       "      <td>4717.511110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-06-01</th>\n",
       "      <td>5312.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>22.832011</td>\n",
       "      <td>4906.404129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-07-01</th>\n",
       "      <td>4298.0</td>\n",
       "      <td>-335.0</td>\n",
       "      <td>22.805572</td>\n",
       "      <td>4685.436707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-08-01</th>\n",
       "      <td>1413.0</td>\n",
       "      <td>-246.0</td>\n",
       "      <td>22.794088</td>\n",
       "      <td>2232.408632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-09-01</th>\n",
       "      <td>5877.0</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales  Seasonal First Difference   forecast  forecast_sarimax\n",
       "Month                                                                     \n",
       "1972-05-01  4618.0                     -392.0  22.892883       4717.511110\n",
       "1972-06-01  5312.0                      438.0  22.832011       4906.404129\n",
       "1972-07-01  4298.0                     -335.0  22.805572       4685.436707\n",
       "1972-08-01  1413.0                     -246.0  22.794088       2232.408632\n",
       "1972-09-01  5877.0                      -74.0        NaN               NaN"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Timestamp('1972-09-01 00:00:00'),\n",
       " Timestamp('1972-10-01 00:00:00'),\n",
       " Timestamp('1972-11-01 00:00:00'),\n",
       " Timestamp('1972-12-01 00:00:00'),\n",
       " Timestamp('1973-01-01 00:00:00'),\n",
       " Timestamp('1973-02-01 00:00:00'),\n",
       " Timestamp('1973-03-01 00:00:00'),\n",
       " Timestamp('1973-04-01 00:00:00'),\n",
       " Timestamp('1973-05-01 00:00:00'),\n",
       " Timestamp('1973-06-01 00:00:00'),\n",
       " Timestamp('1973-07-01 00:00:00'),\n",
       " Timestamp('1973-08-01 00:00:00'),\n",
       " Timestamp('1973-09-01 00:00:00'),\n",
       " Timestamp('1973-10-01 00:00:00'),\n",
       " Timestamp('1973-11-01 00:00:00'),\n",
       " Timestamp('1973-12-01 00:00:00'),\n",
       " Timestamp('1974-01-01 00:00:00'),\n",
       " Timestamp('1974-02-01 00:00:00'),\n",
       " Timestamp('1974-03-01 00:00:00'),\n",
       " Timestamp('1974-04-01 00:00:00'),\n",
       " Timestamp('1974-05-01 00:00:00'),\n",
       " Timestamp('1974-06-01 00:00:00'),\n",
       " Timestamp('1974-07-01 00:00:00'),\n",
       " Timestamp('1974-08-01 00:00:00')]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_dates # hence we will remove the 0th index of this timestamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 4)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>sales</th>\n",
       "      <th>Seasonal First Difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>forecast_sarimax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1972-10-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-02-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-10-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-02-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sales Seasonal First Difference forecast forecast_sarimax\n",
       "1972-10-01   NaN                       NaN      NaN              NaN\n",
       "1972-11-01   NaN                       NaN      NaN              NaN\n",
       "1972-12-01   NaN                       NaN      NaN              NaN\n",
       "1973-01-01   NaN                       NaN      NaN              NaN\n",
       "1973-02-01   NaN                       NaN      NaN              NaN\n",
       "1973-03-01   NaN                       NaN      NaN              NaN\n",
       "1973-04-01   NaN                       NaN      NaN              NaN\n",
       "1973-05-01   NaN                       NaN      NaN              NaN\n",
       "1973-06-01   NaN                       NaN      NaN              NaN\n",
       "1973-07-01   NaN                       NaN      NaN              NaN\n",
       "1973-08-01   NaN                       NaN      NaN              NaN\n",
       "1973-09-01   NaN                       NaN      NaN              NaN\n",
       "1973-10-01   NaN                       NaN      NaN              NaN\n",
       "1973-11-01   NaN                       NaN      NaN              NaN\n",
       "1973-12-01   NaN                       NaN      NaN              NaN\n",
       "1974-01-01   NaN                       NaN      NaN              NaN\n",
       "1974-02-01   NaN                       NaN      NaN              NaN\n",
       "1974-03-01   NaN                       NaN      NaN              NaN\n",
       "1974-04-01   NaN                       NaN      NaN              NaN\n",
       "1974-05-01   NaN                       NaN      NaN              NaN\n",
       "1974-06-01   NaN                       NaN      NaN              NaN\n",
       "1974-07-01   NaN                       NaN      NaN              NaN\n",
       "1974-08-01   NaN                       NaN      NaN              NaN"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_dataset_df = pd.DataFrame(index=future_dates[1:], columns=df.columns)\n",
    "future_dataset_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>sales</th>\n",
       "      <th>Seasonal First Difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>forecast_sarimax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1964-01-01</th>\n",
       "      <td>2815.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-02-01</th>\n",
       "      <td>2672.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-03-01</th>\n",
       "      <td>2755.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-04-01</th>\n",
       "      <td>2721.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1964-05-01</th>\n",
       "      <td>2946.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales  Seasonal First Difference  forecast  forecast_sarimax\n",
       "1964-01-01  2815.0                        NaN       NaN               NaN\n",
       "1964-02-01  2672.0                        NaN       NaN               NaN\n",
       "1964-03-01  2755.0                        NaN       NaN               NaN\n",
       "1964-04-01  2721.0                        NaN       NaN               NaN\n",
       "1964-05-01  2946.0                        NaN       NaN               NaN\n",
       "...            ...                        ...       ...               ...\n",
       "1974-04-01     NaN                        NaN       NaN               NaN\n",
       "1974-05-01     NaN                        NaN       NaN               NaN\n",
       "1974-06-01     NaN                        NaN       NaN               NaN\n",
       "1974-07-01     NaN                        NaN       NaN               NaN\n",
       "1974-08-01     NaN                        NaN       NaN               NaN\n",
       "\n",
       "[128 rows x 4 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_df=pd.concat([df,future_dataset_df])\n",
    "future_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(future_df) # previously it was 105 + 23 months more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "future_df['forecast'] = model_fit_sarimax.predict(start=104,end=120,dynamic=True)\n",
    "future_df[['sales','forecast']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>sales</th>\n",
       "      <th>Seasonal First Difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>forecast_sarimax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971-05-01</th>\n",
       "      <td>5010.0</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-06-01</th>\n",
       "      <td>4874.0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-07-01</th>\n",
       "      <td>4633.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4437.325045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-08-01</th>\n",
       "      <td>1659.0</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2050.747711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-09-01</th>\n",
       "      <td>5951.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5493.376638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-10-01</th>\n",
       "      <td>6981.0</td>\n",
       "      <td>557.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6752.038830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-11-01</th>\n",
       "      <td>9851.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10327.297241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-12-01</th>\n",
       "      <td>12670.0</td>\n",
       "      <td>-406.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13632.323918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>4348.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3832.961477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-02-01</th>\n",
       "      <td>3564.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3313.097072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-03-01</th>\n",
       "      <td>4577.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4285.476182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-04-01</th>\n",
       "      <td>4788.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4701.092714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-05-01</th>\n",
       "      <td>4618.0</td>\n",
       "      <td>-392.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4717.511110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-06-01</th>\n",
       "      <td>5312.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4906.404129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-07-01</th>\n",
       "      <td>4298.0</td>\n",
       "      <td>-335.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4685.436707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-08-01</th>\n",
       "      <td>1413.0</td>\n",
       "      <td>-246.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2232.408632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-09-01</th>\n",
       "      <td>5877.0</td>\n",
       "      <td>-74.0</td>\n",
       "      <td>5878.711943</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-10-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7024.263137</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9996.419824</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12892.152795</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4561.509518</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-02-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3718.817650</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4792.361042</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5034.610506</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5047.956726</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5488.573295</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4593.627869</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1676.138578</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6146.224173</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-10-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7262.512482</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10194.735824</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13057.412446</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4731.201038</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-02-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sales  Seasonal First Difference      forecast  forecast_sarimax\n",
       "1971-05-01   5010.0                     2083.0           NaN               NaN\n",
       "1971-06-01   4874.0                      888.0           NaN               NaN\n",
       "1971-07-01   4633.0                      416.0           NaN       4437.325045\n",
       "1971-08-01   1659.0                      -79.0           NaN       2050.747711\n",
       "1971-09-01   5951.0                      730.0           NaN       5493.376638\n",
       "1971-10-01   6981.0                      557.0           NaN       6752.038830\n",
       "1971-11-01   9851.0                        9.0           NaN      10327.297241\n",
       "1971-12-01  12670.0                     -406.0           NaN      13632.323918\n",
       "1972-01-01   4348.0                      414.0           NaN       3832.961477\n",
       "1972-02-01   3564.0                      402.0           NaN       3313.097072\n",
       "1972-03-01   4577.0                      291.0           NaN       4285.476182\n",
       "1972-04-01   4788.0                      112.0           NaN       4701.092714\n",
       "1972-05-01   4618.0                     -392.0           NaN       4717.511110\n",
       "1972-06-01   5312.0                      438.0           NaN       4906.404129\n",
       "1972-07-01   4298.0                     -335.0           NaN       4685.436707\n",
       "1972-08-01   1413.0                     -246.0           NaN       2232.408632\n",
       "1972-09-01   5877.0                      -74.0   5878.711943               NaN\n",
       "1972-10-01      NaN                        NaN   7024.263137               NaN\n",
       "1972-11-01      NaN                        NaN   9996.419824               NaN\n",
       "1972-12-01      NaN                        NaN  12892.152795               NaN\n",
       "1973-01-01      NaN                        NaN   4561.509518               NaN\n",
       "1973-02-01      NaN                        NaN   3718.817650               NaN\n",
       "1973-03-01      NaN                        NaN   4792.361042               NaN\n",
       "1973-04-01      NaN                        NaN   5034.610506               NaN\n",
       "1973-05-01      NaN                        NaN   5047.956726               NaN\n",
       "1973-06-01      NaN                        NaN   5488.573295               NaN\n",
       "1973-07-01      NaN                        NaN   4593.627869               NaN\n",
       "1973-08-01      NaN                        NaN   1676.138578               NaN\n",
       "1973-09-01      NaN                        NaN   6146.224173               NaN\n",
       "1973-10-01      NaN                        NaN   7262.512482               NaN\n",
       "1973-11-01      NaN                        NaN  10194.735824               NaN\n",
       "1973-12-01      NaN                        NaN  13057.412446               NaN\n",
       "1974-01-01      NaN                        NaN   4731.201038               NaN\n",
       "1974-02-01      NaN                        NaN           NaN               NaN\n",
       "1974-03-01      NaN                        NaN           NaN               NaN\n",
       "1974-04-01      NaN                        NaN           NaN               NaN\n",
       "1974-05-01      NaN                        NaN           NaN               NaN\n",
       "1974-06-01      NaN                        NaN           NaN               NaN\n",
       "1974-07-01      NaN                        NaN           NaN               NaN\n",
       "1974-08-01      NaN                        NaN           NaN               NaN"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_df.tail(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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