TSAF_Monthly_Milk_Productions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task:-> Forecasting_Monthly_Milk_Productions\n",
    "### Dataset:- monthly_milk_production data from 1962 to 1975\n",
    "### Model:- ARIMA and SARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as  np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Dataset\n",
    "df = pd.read_csv(\"./Data/monthly_milk_production.csv\",index_col='Date',parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "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>Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01</th>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02-01</th>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01</th>\n",
       "      <td>640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01</th>\n",
       "      <td>656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01</th>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Production\n",
       "Date                  \n",
       "1962-01-01         589\n",
       "1962-02-01         561\n",
       "1962-03-01         640\n",
       "1962-04-01         656\n",
       "1962-05-01         727"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Production    168\n",
       "dtype: int64"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values\n",
    "df.isnull().count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "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>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>168.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>754.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>102.204524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>553.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>677.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>761.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>824.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>969.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Production\n",
       "count  168.000000\n",
       "mean   754.708333\n",
       "std    102.204524\n",
       "min    553.000000\n",
       "25%    677.750000\n",
       "50%    761.000000\n",
       "75%    824.500000\n",
       "max    969.000000"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statics data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data plotting\n",
    "df.plot(figsize=(12,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setting of frequency\n",
    "df.index.freq='MS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1962-01-01', '1962-02-01', '1962-03-01', '1962-04-01',\n",
       "               '1962-05-01', '1962-06-01', '1962-07-01', '1962-08-01',\n",
       "               '1962-09-01', '1962-10-01',\n",
       "               ...\n",
       "               '1975-03-01', '1975-04-01', '1975-05-01', '1975-06-01',\n",
       "               '1975-07-01', '1975-08-01', '1975-09-01', '1975-10-01',\n",
       "               '1975-11-01', '1975-12-01'],\n",
       "              dtype='datetime64[ns]', name='Date', length=168, freq='MS')"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Yearly plotting of Data\n",
    "\n",
    "from pandas import Grouper\n",
    "import matplotlib.pyplot as plt\n",
    "groups = df['1964':'1970'].groupby(Grouper(freq='A'))\n",
    "years = pd.DataFrame()\n",
    "plt.figure()\n",
    "i = 1\n",
    "n_groups = len(groups)\n",
    "for name, group in groups:\n",
    "    plt.subplot((n_groups*100) + 10 + i)\n",
    "    i += 1\n",
    "    plt.plot(group)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differencing the data for stationarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a differenced df\n",
    "\n",
    "def difference(dataset, interval=1):\n",
    "    diff = list()\n",
    "    for i in range(interval, len(dataset)):\n",
    "        value = dataset.iloc[i] - dataset.iloc[i - interval]\n",
    "        diff.append(value)\n",
    "    return diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Invert Differenced Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inverse_difference(history, yhat, interval=0):\n",
    "    yhat['Production_org']= history['Production'].iloc[0] + yhat['Production_1d'].cumsum()\n",
    "    return yhat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ADF Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "def adf_test(series,title=''):\n",
    "    \"\"\"\n",
    "    Pass in a time series and an optional title, returns an ADF report\n",
    "    \"\"\"\n",
    "    print(f'Augmented Dickey-Fuller Test: {title}')\n",
    "    result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n",
    "    \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "\n",
    "    for key,val in result[4].items():\n",
    "        out[f'critical value ({key})']=val\n",
    "        \n",
    "    print(out.to_string())          # .to_string() removes the line \"dtype: float64\"\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Strong evidence against the null hypothesis\")\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"Weak evidence against the null hypothesis\")\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data has a unit root and is non-stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic       -1.303812\n",
      "p-value                   0.627427\n",
      "# lags used              13.000000\n",
      "# observations          154.000000\n",
      "critical value (1%)      -3.473543\n",
      "critical value (5%)      -2.880498\n",
      "critical value (10%)     -2.576878\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n"
     ]
    }
   ],
   "source": [
    "# Lets check the stationarity of series\n",
    "adf_test(df['Production'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let take first difference of the data\n",
    "df_fod = difference(df['Production'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "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>Production_1d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-02-01</th>\n",
       "      <td>-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01</th>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-06-01</th>\n",
       "      <td>-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Production_1d\n",
       "1962-02-01            -28\n",
       "1962-03-01             79\n",
       "1962-04-01             16\n",
       "1962-05-01             71\n",
       "1962-06-01            -30"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting the data into dataframe\n",
    "\n",
    "idx = pd.date_range('01/02/1962','1975-12-01', freq='MS')\n",
    "df_forecast = pd.DataFrame(df_fod, index=idx, columns=['Production_1d'])\n",
    "df_forecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "# res = inverse_difference(df,df_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: \n",
      "ADF test statistic       -3.054996\n",
      "p-value                   0.030068\n",
      "# lags used              14.000000\n",
      "# observations          152.000000\n",
      "critical value (1%)      -3.474121\n",
      "critical value (5%)      -2.880750\n",
      "critical value (10%)     -2.577013\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "# Performing the ADF Test\n",
    "\n",
    "adf_test(df_forecast['Production_1d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "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": [
    "# Loading the specific forecasting tools\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "plot_acf(df['Production']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 296,
     "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": [
    "# Partial Autocorrelation plot\n",
    "plot_pacf(df['Production'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train_Test_Spliting of data for Validation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = 12\n",
    "train, test = df_forecast['Production_1d'][:-train_size], df_forecast['Production_1d'][-train_size:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search ARIMA Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 1) RMSE=48.617\n",
      "ARIMA(0, 0, 2) RMSE=46.069\n",
      "ARIMA(0, 0, 3) RMSE=41.806\n",
      "ARIMA(0, 0, 4) RMSE=39.583\n",
      "ARIMA(0, 0, 5) RMSE=37.429\n",
      "ARIMA(0, 0, 6) RMSE=44.625\n",
      "ARIMA(0, 1, 1) RMSE=48.447\n",
      "ARIMA(0, 1, 2) RMSE=48.701\n",
      "ARIMA(0, 1, 3) RMSE=46.169\n",
      "ARIMA(0, 1, 4) RMSE=42.664\n",
      "ARIMA(0, 1, 5) RMSE=38.942\n",
      "ARIMA(0, 1, 6) RMSE=39.807\n",
      "ARIMA(0, 2, 1) RMSE=72.404\n",
      "ARIMA(0, 2, 2) RMSE=48.606\n",
      "ARIMA(0, 2, 3) RMSE=48.834\n",
      "ARIMA(0, 2, 4) RMSE=46.589\n",
      "ARIMA(0, 2, 5) RMSE=52.110\n",
      "ARIMA(1, 0, 0) RMSE=48.733\n",
      "ARIMA(1, 0, 1) RMSE=48.909\n",
      "ARIMA(1, 0, 2) RMSE=45.926\n",
      "ARIMA(1, 0, 3) RMSE=41.152\n",
      "ARIMA(1, 0, 4) RMSE=38.355\n",
      "ARIMA(1, 0, 5) RMSE=35.497\n",
      "ARIMA(1, 0, 6) RMSE=35.206\n",
      "ARIMA(1, 1, 0) RMSE=54.372\n",
      "ARIMA(1, 1, 1) RMSE=53.708\n",
      "ARIMA(1, 2, 0) RMSE=80.677\n",
      "ARIMA(2, 0, 0) RMSE=47.265\n",
      "ARIMA(2, 0, 1) RMSE=47.358\n",
      "ARIMA(2, 0, 2) RMSE=45.898\n",
      "ARIMA(2, 0, 3) RMSE=40.717\n",
      "ARIMA(2, 0, 4) RMSE=38.824\n",
      "ARIMA(2, 0, 5) RMSE=38.998\n",
      "ARIMA(2, 0, 6) RMSE=37.680\n",
      "ARIMA(2, 1, 0) RMSE=53.574\n",
      "ARIMA(2, 1, 1) RMSE=47.387\n",
      "ARIMA(2, 2, 0) RMSE=63.180\n",
      "ARIMA(2, 2, 1) RMSE=53.754\n",
      "ARIMA(3, 0, 0) RMSE=47.634\n",
      "ARIMA(3, 0, 1) RMSE=40.530\n",
      "ARIMA(3, 0, 2) RMSE=33.777\n",
      "ARIMA(3, 0, 3) RMSE=32.656\n",
      "ARIMA(3, 0, 4) RMSE=39.980\n",
      "ARIMA(3, 0, 5) RMSE=40.047\n",
      "ARIMA(3, 0, 6) RMSE=30.211\n",
      "ARIMA(3, 1, 0) RMSE=53.142\n",
      "ARIMA(3, 1, 1) RMSE=47.707\n",
      "ARIMA(3, 2, 0) RMSE=63.290\n",
      "ARIMA(3, 2, 1) RMSE=53.321\n",
      "ARIMA(4, 0, 0) RMSE=42.295\n",
      "ARIMA(4, 0, 1) RMSE=45.011\n",
      "ARIMA(4, 0, 2) RMSE=36.365\n",
      "ARIMA(4, 0, 5) RMSE=29.482\n",
      "ARIMA(4, 1, 0) RMSE=50.204\n",
      "ARIMA(4, 1, 1) RMSE=42.370\n",
      "ARIMA(4, 1, 4) RMSE=34.713\n",
      "ARIMA(4, 2, 0) RMSE=58.893\n",
      "ARIMA(4, 2, 1) RMSE=50.354\n",
      "ARIMA(5, 0, 0) RMSE=42.456\n",
      "ARIMA(5, 0, 1) RMSE=37.255\n",
      "ARIMA(5, 0, 2) RMSE=36.519\n",
      "ARIMA(5, 0, 3) RMSE=33.247\n",
      "ARIMA(5, 0, 5) RMSE=27.398\n",
      "ARIMA(5, 0, 6) RMSE=26.301\n",
      "ARIMA(5, 1, 0) RMSE=50.311\n",
      "ARIMA(5, 1, 1) RMSE=42.552\n",
      "ARIMA(5, 1, 3) RMSE=35.008\n",
      "ARIMA(5, 2, 0) RMSE=58.844\n",
      "ARIMA(5, 2, 1) RMSE=50.467\n",
      "ARIMA(6, 0, 0) RMSE=39.690\n",
      "ARIMA(6, 0, 1) RMSE=33.999\n",
      "ARIMA(6, 0, 4) RMSE=26.914\n",
      "ARIMA(6, 0, 5) RMSE=24.567\n",
      "ARIMA(6, 1, 0) RMSE=48.660\n",
      "ARIMA(6, 1, 1) RMSE=39.753\n",
      "ARIMA(6, 1, 4) RMSE=31.149\n",
      "ARIMA(6, 2, 0) RMSE=56.665\n",
      "ARIMA(6, 2, 1) RMSE=48.805\n",
      "Best ARIMA(6, 0, 5) RMSE=24.567\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "import numpy\n",
    "\n",
    " \n",
    "# evaluate an ARIMA model for a given order (p,d,q) and return RMSE\n",
    "def evaluate_arima_model(X, arima_order):\n",
    "    history = [x for x in train]\n",
    "    # make predictions\n",
    "    predictions = list()\n",
    "    for t in range(len(test)):\n",
    "        # difference data\n",
    "        model = ARIMA(history, order=arima_order)\n",
    "        model_fit = model.fit(trend='nc', disp=0)\n",
    "        yhat = model_fit.forecast()[0]\n",
    "        predictions.append(yhat)\n",
    "        history.append(test[t])\n",
    "    # calculate out of sample error\n",
    "    mse = mean_squared_error(test, predictions)\n",
    "    rmse = sqrt(mse)\n",
    "    return rmse\n",
    " \n",
    "# evaluate combinations of p, d and q values for an ARIMA model\n",
    "def evaluate_models(dataset, p_values, d_values, q_values):\n",
    "    dataset = dataset.astype('float32')\n",
    "    best_score, best_cfg = float(\"inf\"), None\n",
    "    for p in p_values:\n",
    "        for d in d_values:\n",
    "            for q in q_values:\n",
    "                order = (p,d,q)\n",
    "                try:\n",
    "                    mse = evaluate_arima_model(dataset, order)\n",
    "                    if mse < best_score:\n",
    "                        best_score, best_cfg = mse, order\n",
    "                    print('ARIMA%s RMSE=%.3f' % (order,mse))\n",
    "                except:\n",
    "                    continue\n",
    "    print('Best ARIMA%s RMSE=%.3f' % (best_cfg, best_score))\n",
    " \n",
    "# load dataset\n",
    "# evaluate parameters\n",
    "p_values = range(0, 7)\n",
    "d_values = range(0, 3)\n",
    "q_values = range(0, 7)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "evaluate_models(train, p_values, d_values, q_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auto Arima Model For Order Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>168</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(3, 1, 2)</td> <th>  Log Likelihood     </th> <td>-806.604</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Thu, 20 Feb 2020</td> <th>  AIC                </th> <td>1627.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>13:36:24</td>     <th>  BIC                </th> <td>1649.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>1636.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 168</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>    0.5630</td> <td>    0.233</td> <td>    2.412</td> <td> 0.016</td> <td>    0.106</td> <td>    1.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>    1.2289</td> <td>    0.103</td> <td>   11.972</td> <td> 0.000</td> <td>    1.028</td> <td>    1.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>     <td>   -0.1609</td> <td>    0.172</td> <td>   -0.938</td> <td> 0.348</td> <td>   -0.497</td> <td>    0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>     <td>   -0.4504</td> <td>    0.098</td> <td>   -4.608</td> <td> 0.000</td> <td>   -0.642</td> <td>   -0.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>     <td>   -1.9125</td> <td>    0.184</td> <td>  -10.413</td> <td> 0.000</td> <td>   -2.273</td> <td>   -1.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>     <td>    0.9964</td> <td>    0.191</td> <td>    5.223</td> <td> 0.000</td> <td>    0.622</td> <td>    1.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>  861.1362</td> <td>  191.188</td> <td>    4.504</td> <td> 0.000</td> <td>  486.414</td> <td> 1235.859</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>510.52</td> <th>  Jarque-Bera (JB):  </th> <td>28.69</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>1.20</td>  <th>  Skew:              </th> <td>-0.89</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>     <td>0.49</td>  <th>  Kurtosis:          </th> <td>3.97</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:                      y   No. Observations:                  168\n",
       "Model:               SARIMAX(3, 1, 2)   Log Likelihood                -806.604\n",
       "Date:                Thu, 20 Feb 2020   AIC                           1627.208\n",
       "Time:                        13:36:24   BIC                           1649.034\n",
       "Sample:                             0   HQIC                          1636.066\n",
       "                                - 168                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept      0.5630      0.233      2.412      0.016       0.106       1.021\n",
       "ar.L1          1.2289      0.103     11.972      0.000       1.028       1.430\n",
       "ar.L2         -0.1609      0.172     -0.938      0.348      -0.497       0.175\n",
       "ar.L3         -0.4504      0.098     -4.608      0.000      -0.642      -0.259\n",
       "ma.L1         -1.9125      0.184    -10.413      0.000      -2.273      -1.553\n",
       "ma.L2          0.9964      0.191      5.223      0.000       0.622       1.370\n",
       "sigma2       861.1362    191.188      4.504      0.000     486.414    1235.859\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                      510.52   Jarque-Bera (JB):                28.69\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.20   Skew:                            -0.89\n",
       "Prob(H) (two-sided):                  0.49   Kurtosis:                         3.97\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pmdarima \n",
    "from pmdarima import auto_arima\n",
    "auto_arima(df['Production'],seasonal=False).summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ARIMA MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ARIMA(train, order=(6,0,5))\n",
    "model_fit =  model.fit(trend='nc', disp=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting the value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predictions = model_fit.predict(start=start, end=end, dynamic=False, typ='levels')\n",
    "# test_fore = model_fit.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1975-01-01    15.080778\n",
       "1975-02-01     3.410053\n",
       "1975-03-01    72.944035\n",
       "1975-04-01    -2.266805\n",
       "1975-05-01    53.191754\n",
       "1975-06-01   -23.347826\n",
       "1975-07-01   -25.188947\n",
       "1975-08-01   -15.638229\n",
       "1975-09-01   -73.421409\n",
       "1975-10-01     7.303473\n",
       "1975-11-01   -50.845784\n",
       "1975-12-01    22.001337\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1975-01-01     21\n",
       "1975-02-01    -52\n",
       "1975-03-01    110\n",
       "1975-04-01     11\n",
       "1975-05-01     63\n",
       "1975-06-01    -29\n",
       "1975-07-01    -41\n",
       "1975-08-01    -38\n",
       "1975-09-01    -41\n",
       "1975-10-01     10\n",
       "1975-11-01    -30\n",
       "1975-12-01     46\n",
       "Freq: MS, Name: Production_1d, dtype: int64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting The Graph between test value and predicted value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph plotting\n",
    "test.plot(figsize=(12,6));\n",
    "predictions.plot(figsize=(12,5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(6,0,5) RMSE Error: 25.16868465\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "error = rmse(test, predictions)\n",
    "print(f'ARIMA(6,0,5) RMSE Error: {error:11.10}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Production    858.846154\n",
       "dtype: float64"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[-13:].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "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>Production_1d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>15.080778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-02-01</th>\n",
       "      <td>3.410053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-03-01</th>\n",
       "      <td>72.944035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-04-01</th>\n",
       "      <td>-2.266805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-05-01</th>\n",
       "      <td>53.191754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-06-01</th>\n",
       "      <td>-23.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-07-01</th>\n",
       "      <td>-25.188947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-08-01</th>\n",
       "      <td>-15.638229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-09-01</th>\n",
       "      <td>-73.421409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-10-01</th>\n",
       "      <td>7.303473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-11-01</th>\n",
       "      <td>-50.845784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-12-01</th>\n",
       "      <td>22.001337</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Production_1d\n",
       "1975-01-01      15.080778\n",
       "1975-02-01       3.410053\n",
       "1975-03-01      72.944035\n",
       "1975-04-01      -2.266805\n",
       "1975-05-01      53.191754\n",
       "1975-06-01     -23.347826\n",
       "1975-07-01     -25.188947\n",
       "1975-08-01     -15.638229\n",
       "1975-09-01     -73.421409\n",
       "1975-10-01       7.303473\n",
       "1975-11-01     -50.845784\n",
       "1975-12-01      22.001337"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.date_range('01/01/1975', periods=12, freq='MS')\n",
    "df_forecast = pd.DataFrame(predictions, index=idx, columns=['Production_1d'])\n",
    "df_forecast"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverse the difference for original Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inverse_difference(history, yhat, interval=-13):\n",
    "    yhat['Production_org']= history['Production'].iloc[interval] + yhat['Production_1d'].cumsum()\n",
    "    return yhat\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_true = inverse_difference(df,df_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "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>Production_1d</th>\n",
       "      <th>Production_org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>15.080778</td>\n",
       "      <td>828.080778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-02-01</th>\n",
       "      <td>3.410053</td>\n",
       "      <td>831.490831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-03-01</th>\n",
       "      <td>72.944035</td>\n",
       "      <td>904.434866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-04-01</th>\n",
       "      <td>-2.266805</td>\n",
       "      <td>902.168061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-05-01</th>\n",
       "      <td>53.191754</td>\n",
       "      <td>955.359815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-06-01</th>\n",
       "      <td>-23.347826</td>\n",
       "      <td>932.011989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-07-01</th>\n",
       "      <td>-25.188947</td>\n",
       "      <td>906.823041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-08-01</th>\n",
       "      <td>-15.638229</td>\n",
       "      <td>891.184812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-09-01</th>\n",
       "      <td>-73.421409</td>\n",
       "      <td>817.763403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-10-01</th>\n",
       "      <td>7.303473</td>\n",
       "      <td>825.066877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-11-01</th>\n",
       "      <td>-50.845784</td>\n",
       "      <td>774.221093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-12-01</th>\n",
       "      <td>22.001337</td>\n",
       "      <td>796.222430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Production_1d  Production_org\n",
       "1975-01-01      15.080778      828.080778\n",
       "1975-02-01       3.410053      831.490831\n",
       "1975-03-01      72.944035      904.434866\n",
       "1975-04-01      -2.266805      902.168061\n",
       "1975-05-01      53.191754      955.359815\n",
       "1975-06-01     -23.347826      932.011989\n",
       "1975-07-01     -25.188947      906.823041\n",
       "1975-08-01     -15.638229      891.184812\n",
       "1975-09-01     -73.421409      817.763403\n",
       "1975-10-01       7.303473      825.066877\n",
       "1975-11-01     -50.845784      774.221093\n",
       "1975-12-01      22.001337      796.222430"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph between predicted and original value\n",
    "df[-12:].plot(figsize=(12,7));\n",
    "pred_true['Production_org'].plot(legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full Data Training Of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "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>Production_1d</th>\n",
       "      <th>Production_org</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>15.080778</td>\n",
       "      <td>828.080778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-02-01</th>\n",
       "      <td>3.410053</td>\n",
       "      <td>831.490831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-03-01</th>\n",
       "      <td>72.944035</td>\n",
       "      <td>904.434866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-04-01</th>\n",
       "      <td>-2.266805</td>\n",
       "      <td>902.168061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-05-01</th>\n",
       "      <td>53.191754</td>\n",
       "      <td>955.359815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Production_1d  Production_org\n",
       "1975-01-01      15.080778      828.080778\n",
       "1975-02-01       3.410053      831.490831\n",
       "1975-03-01      72.944035      904.434866\n",
       "1975-04-01      -2.266805      902.168061\n",
       "1975-05-01      53.191754      955.359815"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_forecast.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df_forecast[\"Production_1d\"]\n",
    "model = ARIMA(data, order=(6,0,5))\n",
    "model_fit =  model.fit(trend='nc', disp=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "start=len(df_forecast)\n",
    "end=len(df_forecast)+12\n",
    "final_predictions = model_fit.predict(start=start, end=end, dynamic=False, typ='levels').rename('ARIMA (6,0,5)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_forecast['1975-01-01':].plot(figsize=(12,5));\n",
    "final_predictions.plot(figsize=(12,5),legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                   <td>y</td>                <th>  No. Observations:  </th>    <td>167</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)x(0, 1, [1], 12)</td> <th>  Log Likelihood     </th> <td>-530.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Thu, 20 Feb 2020</td>         <th>  AIC                </th> <td>1068.208</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>16:50:17</td>             <th>  BIC                </th> <td>1080.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                          <td>0</td>                <th>  HQIC               </th> <td>1073.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                              <td> - 167</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                <td>opg</td>               <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>intercept</th> <td>   -0.0211</td> <td>    0.287</td> <td>   -0.073</td> <td> 0.942</td> <td>   -0.584</td> <td>    0.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>     <td>   -0.2254</td> <td>    0.079</td> <td>   -2.838</td> <td> 0.005</td> <td>   -0.381</td> <td>   -0.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th>  <td>   -0.6193</td> <td>    0.071</td> <td>   -8.693</td> <td> 0.000</td> <td>   -0.759</td> <td>   -0.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>   52.6876</td> <td>    5.099</td> <td>   10.332</td> <td> 0.000</td> <td>   42.693</td> <td>   62.682</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>22.08</td> <th>  Jarque-Bera (JB):  </th> <td>35.12</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.99</td>  <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.82</td>  <th>  Skew:              </th> <td>0.74</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.48</td>  <th>  Kurtosis:          </th> <td>4.80</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:                                    y   No. Observations:                  167\n",
       "Model:             SARIMAX(1, 0, 0)x(0, 1, [1], 12)   Log Likelihood                -530.104\n",
       "Date:                              Thu, 20 Feb 2020   AIC                           1068.208\n",
       "Time:                                      16:50:17   BIC                           1080.381\n",
       "Sample:                                           0   HQIC                          1073.152\n",
       "                                              - 167                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept     -0.0211      0.287     -0.073      0.942      -0.584       0.542\n",
       "ar.L1         -0.2254      0.079     -2.838      0.005      -0.381      -0.070\n",
       "ma.S.L12      -0.6193      0.071     -8.693      0.000      -0.759      -0.480\n",
       "sigma2        52.6876      5.099     10.332      0.000      42.693      62.682\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       22.08   Jarque-Bera (JB):                35.12\n",
       "Prob(Q):                              0.99   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.82   Skew:                             0.74\n",
       "Prob(H) (two-sided):                  0.48   Kurtosis:                         4.80\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(df_forecast['Production_1d'],seasonal=True,m=12).summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SARIMAX(2, 0, 0)x(0, 1, [1], 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train and Test Split\n",
    "\n",
    "train_s = df_forecast[:-12]\n",
    "test_s  = df_forecast[-12:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "model = SARIMAX(train_s ,order=(1,0,0),seasonal_order=(0, 1, 1, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting the model\n",
    "model_fits = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>             <td>Production_1d</td>          <th>  No. Observations:  </th>    <td>155</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 0, 0)x(0, 1, [1], 12)</td> <th>  Log Likelihood     </th> <td>-491.269</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                    <td>Thu, 20 Feb 2020</td>         <th>  AIC                </th>  <td>988.537</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                        <td>16:56:52</td>             <th>  BIC                </th>  <td>997.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                     <td>02-01-1962</td>            <th>  HQIC               </th>  <td>992.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                           <td>- 12-01-1974</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>   -0.2512</td> <td>    0.081</td> <td>   -3.093</td> <td> 0.002</td> <td>   -0.410</td> <td>   -0.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>   -0.6090</td> <td>    0.078</td> <td>   -7.858</td> <td> 0.000</td> <td>   -0.761</td> <td>   -0.457</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>   54.2524</td> <td>    5.265</td> <td>   10.303</td> <td> 0.000</td> <td>   43.932</td> <td>   64.573</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>24.00</td> <th>  Jarque-Bera (JB):  </th> <td>38.10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.98</td>  <th>  Prob(JB):          </th> <td>0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.20</td>  <th>  Skew:              </th> <td>0.79</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.52</td>  <th>  Kurtosis:          </th> <td>4.97</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:                        Production_1d   No. Observations:                  155\n",
       "Model:             SARIMAX(1, 0, 0)x(0, 1, [1], 12)   Log Likelihood                -491.269\n",
       "Date:                              Thu, 20 Feb 2020   AIC                            988.537\n",
       "Time:                                      16:56:52   BIC                            997.426\n",
       "Sample:                                  02-01-1962   HQIC                           992.149\n",
       "                                       - 12-01-1974                                         \n",
       "Covariance Type:                                opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.2512      0.081     -3.093      0.002      -0.410      -0.092\n",
       "ma.S.L12      -0.6090      0.078     -7.858      0.000      -0.761      -0.457\n",
       "sigma2        54.2524      5.265     10.303      0.000      43.932      64.573\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       24.00   Jarque-Bera (JB):                38.10\n",
       "Prob(Q):                              0.98   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               1.20   Skew:                             0.79\n",
       "Prob(H) (two-sided):                  0.52   Kurtosis:                         4.97\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fits.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the value\n",
    "start = len(train)\n",
    "end   = len(train)+len(test)-1\n",
    "predictions = model_fits.predict(start,end,typ ='level').rename('SARIMA (1,0,0) Predictions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1975-01-01     25.322615\n",
       "1975-02-01    -44.498382\n",
       "1975-03-01    104.882544\n",
       "1975-04-01     14.273136\n",
       "1975-05-01     63.277272\n",
       "1975-06-01    -26.401414\n",
       "1975-07-01    -41.486187\n",
       "1975-08-01    -40.595873\n",
       "1975-09-01    -49.050368\n",
       "1975-10-01      2.122915\n",
       "1975-11-01    -36.594182\n",
       "1975-12-01     40.010729\n",
       "Freq: MS, Name: SARIMA (1,0,0) Predictions, dtype: float64"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22e2e50c4a8>"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot between test and predicted value\n",
    "test.plot(figsize=(12,5),legend=True)\n",
    "predictions.plot(figsize=(12,5),legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's Seem Nice fiting of graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(6,0,5) RMSE Error: 5.258936889\n"
     ]
    }
   ],
   "source": [
    "# RootMeanSquareError\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "error = rmse(test_s['Production_1d'], predictions)\n",
    "print(f'ARIMA(6,0,5) RMSE Error: {error:11.10}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "model = SARIMAX(df_forecast ,order=(1,0,0),seasonal_order=(0, 1, 1, 12))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_final = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prediction \n",
    "start = len(df_forecast)\n",
    "end   = len(df_forecast)+11\n",
    "final_forecast = result_final.predict(start,end,typ ='level')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1976-01-01     22.349579\n",
       "1976-02-01    -46.962221\n",
       "1976-03-01    106.632007\n",
       "1976-04-01     13.077018\n",
       "1976-05-01     63.141984\n",
       "1976-06-01    -27.408297\n",
       "1976-07-01    -41.352506\n",
       "1976-08-01    -39.588514\n",
       "1976-09-01    -45.894729\n",
       "1976-10-01      5.171799\n",
       "1976-11-01    -34.035630\n",
       "1976-12-01     42.278132\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_forecast.plot(figsize=(12,5),legend=True)\n",
    "final_forecast.plot(figsize=(12,5),legend=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inverting The data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "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>final_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>22.349579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-02-01</th>\n",
       "      <td>-46.962221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-03-01</th>\n",
       "      <td>106.632007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-04-01</th>\n",
       "      <td>13.077018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-05-01</th>\n",
       "      <td>63.141984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-06-01</th>\n",
       "      <td>-27.408297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-07-01</th>\n",
       "      <td>-41.352506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-08-01</th>\n",
       "      <td>-39.588514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-09-01</th>\n",
       "      <td>-45.894729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-10-01</th>\n",
       "      <td>5.171799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-11-01</th>\n",
       "      <td>-34.035630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-12-01</th>\n",
       "      <td>42.278132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            final_pred\n",
       "1976-01-01   22.349579\n",
       "1976-02-01  -46.962221\n",
       "1976-03-01  106.632007\n",
       "1976-04-01   13.077018\n",
       "1976-05-01   63.141984\n",
       "1976-06-01  -27.408297\n",
       "1976-07-01  -41.352506\n",
       "1976-08-01  -39.588514\n",
       "1976-09-01  -45.894729\n",
       "1976-10-01    5.171799\n",
       "1976-11-01  -34.035630\n",
       "1976-12-01   42.278132"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.date_range('01/01/1976', periods=12, freq='MS')\n",
    "df_fore = pd.DataFrame(final_forecast, index=idx, columns=['final_pred'])\n",
    "df_fore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1974-12-01    813\n",
       "1975-01-01    834\n",
       "1975-02-01    782\n",
       "1975-03-01    892\n",
       "1975-04-01    903\n",
       "1975-05-01    966\n",
       "1975-06-01    937\n",
       "1975-07-01    896\n",
       "1975-08-01    858\n",
       "1975-09-01    817\n",
       "1975-10-01    827\n",
       "1975-11-01    797\n",
       "1975-12-01    843\n",
       "Freq: MS, Name: Production, dtype: int64"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Production'][-13:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inverse_difference_final(history, yhat, interval=-1):\n",
    "    yhat['Production_forcast']= history['Production'].iloc[interval] + yhat['final_pred'].cumsum()\n",
    "    return yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_inv = inverse_difference_final(df,df_fore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df['Production']['1973-01-01':].plot(figsize=(12,5),legend=True)\n",
    "final_inv['Production_forcast'].plot(figsize=(12,5),legend=True);\n",
    "ax.autoscale(axis='both', tight=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thanks!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}