11 ARMA and ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARMA, ARIMA, ARMAResults, ARIMAResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "# We have basically imported this so that we can also check out the manual way of determining p,d,q values for ARIMA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Births</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1959-01-01</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1959-01-02</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1959-01-03</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1959-01-04</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1959-01-05</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Births\n",
       "Date              \n",
       "1959-01-01      35\n",
       "1959-01-02      32\n",
       "1959-01-03      30\n",
       "1959-01-04      31\n",
       "1959-01-05      44"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"C:\\\\Users\\\\shaleens\\\\Desktop\\\\Projects\\\\TimeSeriesUdacity\\\\TS Udemy Vids\\\\Time Series 2\\\\DailyFemaleBirths.csv\", index_col=0, parse_dates=True)\n",
    "df1.index.freq = 'D'\n",
    "df1 = df1[:120]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2 = pd.read_csv(\"C:\\\\Users\\\\shaleens\\\\Desktop\\\\Projects\\\\TimeSeriesUdacity\\\\TS Udemy Vids\\\\Time Series 2\\\\\", index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARMA model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d34080f988>"
      ]
     },
     "execution_count": 8,
     "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": [
    "df1['Births'].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Lets check for Stationarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets use our created function to get the analysis out of adfuller test:\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "\n",
    "def adf_test(series, title=''):\n",
    "    '''\n",
    "    Pass in a time series and an optional title, function 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 stats','p-value', '# of lags', '# of 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())\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Null Hypothesis rejected! Time series is stationary!\")\n",
    "    else:\n",
    "        print(\"Failed to reject Null Hypothesis! Time Series is not stationary!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-fuller test: \n",
      "adf test stats         -9.855384e+00\n",
      "p-value                 4.373545e-17\n",
      "# of lags               0.000000e+00\n",
      "# of observations       1.190000e+02\n",
      "critical value (1%)    -3.486535e+00\n",
      "critical value (5%)    -2.886151e+00\n",
      "critical value (10%)   -2.579896e+00\n",
      "Null Hypothesis rejected! Time series is stationary!\n"
     ]
    }
   ],
   "source": [
    "adf_test(df1['Births'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## This confirms that our time series is Stationary.\n",
    "## It means ARMA model should work for this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit ARIMA: (0, 0, 0)x(0, 0, 0, 0) (constant=True); AIC=823.489, BIC=829.064, Time=0.014 seconds\n",
      "Fit ARIMA: (0, 0, 1)x(0, 0, 0, 0) (constant=True); AIC=824.747, BIC=833.110, Time=0.092 seconds\n",
      "Fit ARIMA: (0, 0, 2)x(0, 0, 0, 0) (constant=True); AIC=826.057, BIC=837.207, Time=0.128 seconds\n",
      "Fit ARIMA: (0, 0, 3)x(0, 0, 0, 0) (constant=True); AIC=828.050, BIC=841.987, Time=0.139 seconds\n",
      "Fit ARIMA: (0, 0, 4)x(0, 0, 0, 0) (constant=True); AIC=829.793, BIC=846.518, Time=0.248 seconds\n",
      "Fit ARIMA: (0, 0, 5)x(0, 0, 0, 0) (constant=True); AIC=831.518, BIC=851.030, Time=0.284 seconds\n",
      "Fit ARIMA: (1, 0, 0)x(0, 0, 0, 0) (constant=True); AIC=824.647, BIC=833.010, Time=0.048 seconds\n",
      "Fit ARIMA: (1, 0, 1)x(0, 0, 0, 0) (constant=True); AIC=826.399, BIC=837.549, Time=0.282 seconds\n",
      "Fit ARIMA: (1, 0, 2)x(0, 0, 0, 0) (constant=True); AIC=828.062, BIC=841.999, Time=0.148 seconds\n",
      "Fit ARIMA: (1, 0, 3)x(0, 0, 0, 0) (constant=True); AIC=830.057, BIC=846.782, Time=0.220 seconds\n",
      "Fit ARIMA: (1, 0, 4)x(0, 0, 0, 0) (constant=True); AIC=831.812, BIC=851.324, Time=0.302 seconds\n",
      "Fit ARIMA: (2, 0, 0)x(0, 0, 0, 0) (constant=True); AIC=826.148, BIC=837.298, Time=0.088 seconds\n",
      "Fit ARIMA: (2, 0, 1)x(0, 0, 0, 0) (constant=True); AIC=828.134, BIC=842.072, Time=0.157 seconds\n",
      "Fit ARIMA: (2, 0, 2)x(0, 0, 0, 0) (constant=True); AIC=829.617, BIC=846.342, Time=0.564 seconds\n",
      "Fit ARIMA: (2, 0, 3)x(0, 0, 0, 0) (constant=True); AIC=831.552, BIC=851.065, Time=0.849 seconds\n",
      "Fit ARIMA: (3, 0, 0)x(0, 0, 0, 0) (constant=True); AIC=828.131, BIC=842.068, Time=0.087 seconds\n",
      "Fit ARIMA: (3, 0, 1)x(0, 0, 0, 0) (constant=True); AIC=830.111, BIC=846.836, Time=0.173 seconds\n",
      "Fit ARIMA: (3, 0, 2)x(0, 0, 0, 0) (constant=True); AIC=831.229, BIC=850.741, Time=0.616 seconds\n",
      "Fit ARIMA: (4, 0, 0)x(0, 0, 0, 0) (constant=True); AIC=829.734, BIC=846.459, Time=0.540 seconds\n",
      "Fit ARIMA: (4, 0, 1)x(0, 0, 0, 0) (constant=True); AIC=831.755, BIC=851.267, Time=0.324 seconds\n",
      "Fit ARIMA: (5, 0, 0)x(0, 0, 0, 0) (constant=True); AIC=831.679, BIC=851.192, Time=0.324 seconds\n",
      "Total fit time: 5.634 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>120</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                <td>SARIMAX</td>     <th>  Log Likelihood     </th> <td>-409.745</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 19 Feb 2020</td> <th>  AIC                </th>  <td>823.489</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>19:16:58</td>     <th>  BIC                </th>  <td>829.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>825.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 120</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>   39.7833</td> <td>    0.687</td> <td>   57.896</td> <td> 0.000</td> <td>   38.437</td> <td>   41.130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td>   54.1197</td> <td>    8.319</td> <td>    6.506</td> <td> 0.000</td> <td>   37.815</td> <td>   70.424</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>44.41</td> <th>  Jarque-Bera (JB):  </th> <td>2.69</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.29</td>  <th>  Prob(JB):          </th> <td>0.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.80</td>  <th>  Skew:              </th> <td>0.26</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.48</td>  <th>  Kurtosis:          </th> <td>2.48</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:                  120\n",
       "Model:                        SARIMAX   Log Likelihood                -409.745\n",
       "Date:                Wed, 19 Feb 2020   AIC                            823.489\n",
       "Time:                        19:16:58   BIC                            829.064\n",
       "Sample:                             0   HQIC                           825.753\n",
       "                                - 120                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept     39.7833      0.687     57.896      0.000      38.437      41.130\n",
       "sigma2        54.1197      8.319      6.506      0.000      37.815      70.424\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       44.41   Jarque-Bera (JB):                 2.69\n",
       "Prob(Q):                              0.29   Prob(JB):                         0.26\n",
       "Heteroskedasticity (H):               0.80   Skew:                             0.26\n",
       "Prob(H) (two-sided):                  0.48   Kurtosis:                         2.48\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(df1['Births'],start_p=2,max_p=6, start_q=2, max_q=6, seasonal=False, trace = True, stepwise=False, suppress_warnings=True,).summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Trying using statsmodels:\n",
    "\n",
    "# import statsmodels.api as sm\n",
    "# resDiff = sm.tsa.arma_order_select_ic(df1['Births'], max_ar=4, max_ma=4, ic='aic', trend='c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('ARMA(p,q) =',resDiff['aic_min_order'],'is the best.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seems like, something went wrong while identifying the values of p and q for ARMA model.!!\n",
    "### Performed different perm and comb and considering (p,q) = (1,0) having best AIC until further investigation.!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df1.iloc[:90]\n",
    "test = df1.iloc[90:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1959-01-01    35\n",
       "1959-01-02    32\n",
       "1959-01-03    30\n",
       "1959-01-04    31\n",
       "1959-01-05    44\n",
       "              ..\n",
       "1959-03-27    56\n",
       "1959-03-28    36\n",
       "1959-03-29    32\n",
       "1959-03-30    50\n",
       "1959-03-31    41\n",
       "Freq: D, Name: Births, Length: 90, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Births']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ARMA(train['Births'], order=(1,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>Births</td>      <th>  No. Observations:  </th>    <td>90</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(1, 0)</td>    <th>  Log Likelihood     </th> <td>-308.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>7.417</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 19 Feb 2020</td> <th>  AIC                </th>  <td>622.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>19:16:58</td>     <th>  BIC                </th>  <td>629.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>01-01-1959</td>    <th>  HQIC               </th>  <td>625.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 03-31-1959</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>const</th>        <td>   39.7630</td> <td>    0.853</td> <td>   46.597</td> <td> 0.000</td> <td>   38.091</td> <td>   41.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.Births</th> <td>    0.0847</td> <td>    0.105</td> <td>    0.809</td> <td> 0.419</td> <td>   -0.121</td> <td>    0.290</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>          11.8103</td> <td>          +0.0000j</td> <td>          11.8103</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                 Births   No. Observations:                   90\n",
       "Model:                     ARMA(1, 0)   Log Likelihood                -308.054\n",
       "Method:                       css-mle   S.D. of innovations              7.417\n",
       "Date:                Wed, 19 Feb 2020   AIC                            622.107\n",
       "Time:                        19:16:58   BIC                            629.607\n",
       "Sample:                    01-01-1959   HQIC                           625.132\n",
       "                         - 03-31-1959                                         \n",
       "================================================================================\n",
       "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const           39.7630      0.853     46.597      0.000      38.091      41.436\n",
       "ar.L1.Births     0.0847      0.105      0.809      0.419      -0.121       0.290\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1           11.8103           +0.0000j           11.8103            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 119)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start=len(train)\n",
    "end= len(train) + len(test) - 1\n",
    "start,end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = results.predict(start, end).rename('ARMA(2,2) preds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d34328eec8>"
      ]
     },
     "execution_count": 22,
     "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": [
    "test.plot(figsize=(12,8),legend = True)\n",
    "predictions.plot(legend = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Births    39.833333\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.76685659374244"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.mean()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "## So, we can see ARMA model was not able to predict the noise but it was really close to actually predict the actual mean average for next month of data, which is impressive, given how simple this model is!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARIMA modelling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets look into full ARIMA model now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv(\"C:\\\\Users\\\\shaleens\\\\Desktop\\\\Projects\\\\TimeSeriesUdacity\\\\TS Udemy Vids\\\\Time Series 2\\\\Trade Inventories.csv\", parse_dates=True, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Inventory</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1301161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-02-01</td>\n",
       "      <td>1307080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-03-01</td>\n",
       "      <td>1303978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-04-01</td>\n",
       "      <td>1319740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-05-01</td>\n",
       "      <td>1327294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-06-01</td>\n",
       "      <td>1334688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-07-01</td>\n",
       "      <td>1342310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-08-01</td>\n",
       "      <td>1348243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-09-01</td>\n",
       "      <td>1355839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-10-01</td>\n",
       "      <td>1364970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-11-01</td>\n",
       "      <td>1370492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-12-01</td>\n",
       "      <td>1378022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1998-01-01</td>\n",
       "      <td>1384404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Inventory\n",
       "Date                 \n",
       "1997-01-01    1301161\n",
       "1997-02-01    1307080\n",
       "1997-03-01    1303978\n",
       "1997-04-01    1319740\n",
       "1997-05-01    1327294\n",
       "1997-06-01    1334688\n",
       "1997-07-01    1342310\n",
       "1997-08-01    1348243\n",
       "1997-09-01    1355839\n",
       "1997-10-01    1364970\n",
       "1997-11-01    1370492\n",
       "1997-12-01    1378022\n",
       "1998-01-01    1384404"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.iloc[:264]\n",
    "df2.index.freq= \"MS\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d346342e48>"
      ]
     },
     "execution_count": 112,
     "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": [
    "df2.plot(figsize = (12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-fuller test: \n",
      "adf test stats           -0.101142\n",
      "p-value                   0.949325\n",
      "# of lags                 5.000000\n",
      "# of observations       258.000000\n",
      "critical value (1%)      -3.455953\n",
      "critical value (5%)      -2.872809\n",
      "critical value (10%)     -2.572775\n",
      "Failed to reject Null Hypothesis! Time Series is not stationary!\n"
     ]
    }
   ],
   "source": [
    "adf_test(df2['Inventory'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = seasonal_decompose(df2['Inventory'], model='add')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can see that our data has seasonal component, so we can run SARIMA model as well.\n",
    "\n",
    "# But, for now lets first implement ARIMA model assuming that the magnitude of seasonal component is too small and can be ignored!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pmdarima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      "Fit ARIMA: (2, 1, 2)x(0, 0, 0, 0) (constant=True); AIC=5371.982, BIC=5393.415, Time=0.548 seconds\n",
      "Fit ARIMA: (0, 1, 0)x(0, 0, 0, 0) (constant=True); AIC=5345.497, BIC=5352.641, Time=0.016 seconds\n",
      "Fit ARIMA: (1, 1, 0)x(0, 0, 0, 0) (constant=True); AIC=5398.340, BIC=5409.056, Time=0.047 seconds\n",
      "Fit ARIMA: (0, 1, 1)x(0, 0, 0, 0) (constant=True); AIC=5347.635, BIC=5358.351, Time=0.053 seconds\n",
      "Fit ARIMA: (0, 1, 0)x(0, 0, 0, 0) (constant=False); AIC=5407.713, BIC=5411.285, Time=0.000 seconds\n",
      "Fit ARIMA: (1, 1, 1)x(0, 0, 0, 0) (constant=True); AIC=5377.052, BIC=5391.340, Time=0.249 seconds\n",
      "Total fit time: 0.917 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>264</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 0)</td> <th>  Log Likelihood     </th> <td>-2670.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 19 Feb 2020</td> <th>  AIC                </th> <td>5345.497</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:11:53</td>     <th>  BIC                </th> <td>5352.641</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>5348.368</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 264</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> 3272.5171</td> <td>  472.729</td> <td>    6.923</td> <td> 0.000</td> <td> 2345.985</td> <td> 4199.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 3.873e+07</td> <td> 2.92e+06</td> <td>   13.263</td> <td> 0.000</td> <td>  3.3e+07</td> <td> 4.44e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>461.88</td> <th>  Jarque-Bera (JB):  </th> <td>107.24</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>0.82</td>  <th>  Skew:              </th>  <td>-1.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>     <td>0.35</td>  <th>  Kurtosis:          </th>  <td>5.06</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:                  264\n",
       "Model:               SARIMAX(0, 1, 0)   Log Likelihood               -2670.748\n",
       "Date:                Wed, 19 Feb 2020   AIC                           5345.497\n",
       "Time:                        20:11:53   BIC                           5352.641\n",
       "Sample:                             0   HQIC                          5348.368\n",
       "                                - 264                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   3272.5171    472.729      6.923      0.000    2345.985    4199.050\n",
       "sigma2      3.873e+07   2.92e+06     13.263      0.000     3.3e+07    4.44e+07\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                      461.88   Jarque-Bera (JB):               107.24\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.82   Skew:                            -1.18\n",
       "Prob(H) (two-sided):                  0.35   Kurtosis:                         5.06\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(df2['Inventory'], seasonal=False, trace=True).summary()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The auto_arima result returned (0,1,0) for (p,d,q). It tells us that our data needs to be differenced once.! Also, we observed earlier that our dataset was not stationary!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.statespace.tools import diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Diff_1'] = diff(df2['Inventory'], k_diff=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Inventory</th>\n",
       "      <th>Diff_1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>1301161</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-02-01</td>\n",
       "      <td>1307080</td>\n",
       "      <td>5919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-03-01</td>\n",
       "      <td>1303978</td>\n",
       "      <td>-3102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-04-01</td>\n",
       "      <td>1319740</td>\n",
       "      <td>15762.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1997-05-01</td>\n",
       "      <td>1327294</td>\n",
       "      <td>7554.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Inventory   Diff_1\n",
       "Date                          \n",
       "1997-01-01    1301161      NaN\n",
       "1997-02-01    1307080   5919.0\n",
       "1997-03-01    1303978  -3102.0\n",
       "1997-04-01    1319740  15762.0\n",
       "1997-05-01    1327294   7554.0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-fuller test: \n",
      "adf test stats           -3.426619\n",
      "p-value                   0.010084\n",
      "# of lags                 4.000000\n",
      "# of observations       258.000000\n",
      "critical value (1%)      -3.455953\n",
      "critical value (5%)      -2.872809\n",
      "critical value (10%)     -2.572775\n",
      "Null Hypothesis rejected! Time series is stationary!\n"
     ]
    }
   ],
   "source": [
    "adf_test(df2['Diff_1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets also check manually how we can tell p,d,q values using PACF and ACF plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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": [
    "plot_acf(df2['Inventory'], lags=40);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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": [
    "plot_pacf(df2['Inventory'],lags=40);"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We already came to know that d=1, coz out data turned stationary after 1 differencing.\n",
    "\n",
    "With PACF plot, we can see sharp drop at lag = 1, so we can take p=1\n",
    "whereas we found thet there is the slight and gradual decline in ACF plot, which also means AR term has more impact overall. \n",
    "So, we may take q = 0 or 1, depending on who gives us less error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      "Fit ARIMA: (0, 1, 0)x(0, 0, 0, 0) (constant=True); AIC=5345.497, BIC=5352.641, Time=0.020 seconds\n",
      "Fit ARIMA: (1, 1, 0)x(0, 0, 0, 0) (constant=True); AIC=5398.340, BIC=5409.056, Time=0.072 seconds\n",
      "Fit ARIMA: (0, 1, 1)x(0, 0, 0, 0) (constant=True); AIC=5347.635, BIC=5358.351, Time=0.068 seconds\n",
      "Fit ARIMA: (0, 1, 0)x(0, 0, 0, 0) (constant=False); AIC=5407.713, BIC=5411.285, Time=0.008 seconds\n",
      "Fit ARIMA: (1, 1, 1)x(0, 0, 0, 0) (constant=True); AIC=5377.052, BIC=5391.340, Time=0.227 seconds\n",
      "Total fit time: 0.402 seconds\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>264</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 0)</td> <th>  Log Likelihood     </th> <td>-2670.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 19 Feb 2020</td> <th>  AIC                </th> <td>5345.497</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:12:00</td>     <th>  BIC                </th> <td>5352.641</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>5348.368</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                      <td> - 264</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> 3272.5171</td> <td>  472.729</td> <td>    6.923</td> <td> 0.000</td> <td> 2345.985</td> <td> 4199.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>    <td> 3.873e+07</td> <td> 2.92e+06</td> <td>   13.263</td> <td> 0.000</td> <td>  3.3e+07</td> <td> 4.44e+07</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>461.88</td> <th>  Jarque-Bera (JB):  </th> <td>107.24</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>0.82</td>  <th>  Skew:              </th>  <td>-1.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>     <td>0.35</td>  <th>  Kurtosis:          </th>  <td>5.06</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:                  264\n",
       "Model:               SARIMAX(0, 1, 0)   Log Likelihood               -2670.748\n",
       "Date:                Wed, 19 Feb 2020   AIC                           5345.497\n",
       "Time:                        20:12:00   BIC                           5352.641\n",
       "Sample:                             0   HQIC                          5348.368\n",
       "                                - 264                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "intercept   3272.5171    472.729      6.923      0.000    2345.985    4199.050\n",
       "sigma2      3.873e+07   2.92e+06     13.263      0.000     3.3e+07    4.44e+07\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                      461.88   Jarque-Bera (JB):               107.24\n",
       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):               0.82   Skew:                            -1.18\n",
       "Prob(H) (two-sided):                  0.35   Kurtosis:                         5.06\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stepwise_fit = auto_arima(df2['Inventory'], start_p=0, start_q=0,max_p=2\n",
    "                         ,max_q=2, seasonal=False, trace=True)\n",
    "\n",
    "stepwise_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "264"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df2.iloc[:252]\n",
    "test = df2.iloc[252:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IMPORTANT TIP:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### It is suggested to play around with different values of (p,d,q) here, to check which one is giving the least AIC score!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>D.Inventory</td>   <th>  No. Observations:  </th>    <td>251</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-2486.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th> <td>4838.888</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 19 Feb 2020</td> <th>  AIC                </th> <td>4980.153</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>20:20:58</td>     <th>  BIC                </th> <td>4994.255</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>02-01-1997</td>    <th>  HQIC               </th> <td>4985.828</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 12-01-2017</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>const</th>             <td> 3281.7305</td> <td> 1340.437</td> <td>    2.448</td> <td> 0.014</td> <td>  654.522</td> <td> 5908.939</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Inventory</th> <td>    0.9016</td> <td>    0.039</td> <td>   22.836</td> <td> 0.000</td> <td>    0.824</td> <td>    0.979</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.D.Inventory</th> <td>   -0.5547</td> <td>    0.080</td> <td>   -6.969</td> <td> 0.000</td> <td>   -0.711</td> <td>   -0.399</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th> <td>           1.1092</td> <td>          +0.0000j</td> <td>           1.1092</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th> <td>           1.8026</td> <td>          +0.0000j</td> <td>           1.8026</td> <td>           0.0000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:            D.Inventory   No. Observations:                  251\n",
       "Model:                 ARIMA(1, 1, 1)   Log Likelihood               -2486.076\n",
       "Method:                       css-mle   S.D. of innovations           4838.888\n",
       "Date:                Wed, 19 Feb 2020   AIC                           4980.153\n",
       "Time:                        20:20:58   BIC                           4994.255\n",
       "Sample:                    02-01-1997   HQIC                          4985.828\n",
       "                         - 12-01-2017                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const              3281.7305   1340.437      2.448      0.014     654.522    5908.939\n",
       "ar.L1.D.Inventory     0.9016      0.039     22.836      0.000       0.824       0.979\n",
       "ma.L1.D.Inventory    -0.5547      0.080     -6.969      0.000      -0.711      -0.399\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1            1.1092           +0.0000j            1.1092            0.0000\n",
       "MA.1            1.8026           +0.0000j            1.8026            0.0000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels as sm\n",
    "\n",
    "model = sm.tsa.arima_model.ARIMA(train['Inventory'],order=(1,1,1))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = len(train)\n",
    "end = len(train) + len(test) -1\n",
    "\n",
    "\n",
    "predictions = results.predict(start = start, end = end, typ='levels').rename('Predicted ARIMA(1,1,1)')\n",
    "# typ = 'linear' (i.e. by default gives us predictions in terms of differenced endogeneous variables)\n",
    "# whereas, typ = 'levels' predicts level of original endogeneous variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-01-01    2.111716e+06\n",
       "2018-02-01    2.115378e+06\n",
       "2018-03-01    2.119002e+06\n",
       "2018-04-01    2.122593e+06\n",
       "2018-05-01    2.126153e+06\n",
       "2018-06-01    2.129686e+06\n",
       "2018-07-01    2.133194e+06\n",
       "2018-08-01    2.136679e+06\n",
       "2018-09-01    2.140145e+06\n",
       "2018-10-01    2.143593e+06\n",
       "2018-11-01    2.147024e+06\n",
       "2018-12-01    2.150441e+06\n",
       "Freq: MS, Name: Predicted ARIMA(1,1,1), dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d349e9eb88>"
      ]
     },
     "execution_count": 165,
     "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": [
    "#train['Inventory'].plot(figsize=(12,8), legend=True)\n",
    "test['Inventory'].plot(legend=True)\n",
    "predictions.plot(legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6115.390518090231"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "rmse = np.sqrt(mean_squared_error(test['Inventory'], predictions))\n",
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can compare the average values of test set and predicted values also, to check how off we are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2131300.2512028967"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2131201.25"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Inventory'].mean()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can observe that we have approx 6115 units of error when the actual values ranges more than 20 lakhs, which is still good.\n",
    "Also, its worth noting that we havent included the seasonal parameters to the model yet i.e. (P,D,Q) which we will do it later!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To forecast into the unknown future using the simple ARIMA model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Considering full dataset:\n",
    "\n",
    "model = ARIMA(df2['Inventory'], order=(1,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast = result.predict(start=len(df2), end=len(df2)+11, typ='levels').rename(\"ARIMA(1,1,1) future forecasts\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d3484278c8>"
      ]
     },
     "execution_count": 179,
     "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": [
    "df2['Inventory'].plot(figsize=(12,8), legend=True)\n",
    "forecast.plot(legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So, till now we have performed forecast using basic ARIMA model with order of (1,1,1)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}