univariate-time-series-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-03-28T04:13:52.083674Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.056763Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-03-28T04:13:52.090906Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.086063Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\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>Month</th>\n",
       "      <th>Thousands of Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Thousands of Passengers\n",
       "0  1949-01                      112\n",
       "1  1949-02                      118\n",
       "2  1949-03                      132\n",
       "3  1949-04                      129\n",
       "4  1949-05                      121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"airline_passengers.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2022-03-28T04:13:52.179322Z",
     "shell.execute_reply": "2022-03-28T04:13:52.179176Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.179155Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 144 entries, 0 to 143\n",
      "Data columns (total 2 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   Month                    144 non-null    object\n",
      " 1   Thousands of Passengers  144 non-null    int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 2.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-03-28T04:13:52.181674Z"
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   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Thousands of Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>280.298611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>119.966317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>180.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>265.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>360.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>622.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Thousands of Passengers\n",
       "count               144.000000\n",
       "mean                280.298611\n",
       "std                 119.966317\n",
       "min                 104.000000\n",
       "25%                 180.000000\n",
       "50%                 265.500000\n",
       "75%                 360.500000\n",
       "max                 622.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 144 entries, 1949-01-01 to 1960-12-01\n",
      "Data columns (total 1 columns):\n",
      " #   Column                   Non-Null Count  Dtype\n",
      "---  ------                   --------------  -----\n",
      " 0   Thousands of Passengers  144 non-null    int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 2.2 KB\n"
     ]
    }
   ],
   "source": [
    "df[\"Month\"]=pd.to_datetime(df[\"Month\"])\n",
    "df.set_index(['Month'],inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-03-28T04:13:52.185473Z"
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   },
   "outputs": [
    {
     "data": {
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       "    }\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>Thousands of Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Thousands of Passengers\n",
       "Month                              \n",
       "1949-01-01                      112\n",
       "1949-02-01                      118\n",
       "1949-03-01                      132\n",
       "1949-04-01                      129\n",
       "1949-05-01                      121"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2022-03-28T04:13:52.187745Z",
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     "shell.execute_reply.started": "2022-03-28T04:13:52.187505Z"
    }
   },
   "outputs": [],
   "source": [
    "df.index.freq = 'MS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.188960Z",
     "iopub.status.idle": "2022-03-28T04:13:52.189437Z",
     "shell.execute_reply": "2022-03-28T04:13:52.189206Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.189182Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Thousands of Passengers    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2022-03-28T04:13:52.191053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(df['Thousands of Passengers'])\n",
    "plt.title(\"Monthly total of Airline Passengers\")\n",
    "plt.ylabel(\"In Thousands\")\n",
    "plt.xlabel(\"year\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T04:13:52.308920Z",
     "iopub.status.busy": "2022-03-28T04:13:52.308508Z",
     "iopub.status.idle": "2022-03-28T04:13:52.322394Z",
     "shell.execute_reply": "2022-03-28T04:13:52.321260Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.308883Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1949-12-01', '1950-12-01', '1951-12-01', '1952-12-01',\n",
       "               '1953-12-01', '1954-12-01', '1955-12-01', '1956-12-01',\n",
       "               '1957-12-01', '1958-12-01', '1959-12-01', '1960-12-01'],\n",
       "              dtype='datetime64[ns]', name='Month', freq=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index[df.index.month==12]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seasonality check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.323256Z",
     "iopub.status.idle": "2022-03-28T04:13:52.323603Z",
     "shell.execute_reply": "2022-03-28T04:13:52.323456Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.323438Z"
    }
   },
   "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": [
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(df['Thousands of Passengers'])\n",
    "plt.title(\"Monthly total of Airline Passengers\")\n",
    "plt.ylabel(\"In Thousands\")\n",
    "plt.xlabel(\"year\")\n",
    "for x in df.index[df.index.month==12]:\n",
    "    plt.axvline(x=x, color='red');\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decompose the signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.325072Z",
     "iopub.status.idle": "2022-03-28T04:13:52.325377Z",
     "shell.execute_reply": "2022-03-28T04:13:52.325231Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.325215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "result = seasonal_decompose(df['Thousands of Passengers'], model='additive')\n",
    "fig, axs = plt.subplots(2, 2,figsize=(15,8))\n",
    "axs[0, 0].plot(result.observed)\n",
    "axs[0, 0].autoscale(axis='x',tight=True)\n",
    "axs[0, 0].set_title('Observed')\n",
    "axs[0, 1].plot(result.trend,'tab:orange')\n",
    "axs[0, 1].autoscale(axis='x',tight=True)\n",
    "axs[0, 1].set_title('Trend')\n",
    "axs[1, 0].plot(result.seasonal, 'tab:green')\n",
    "axs[1, 0].autoscale(axis='x',tight=True)\n",
    "axs[1, 0].set_title('Seasonal')\n",
    "axs[1, 1].plot(result.resid, 'tab:red')\n",
    "axs[1, 1].autoscale(axis='x',tight=True)\n",
    "axs[1, 1].set_title('Residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shift the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T04:13:52.329087Z",
     "iopub.status.busy": "2022-03-28T04:13:52.328429Z",
     "iopub.status.idle": "2022-03-28T04:13:52.342694Z",
     "shell.execute_reply": "2022-03-28T04:13:52.341721Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.329032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-01-01      NaN\n",
       "1949-02-01    112.0\n",
       "1949-03-01    118.0\n",
       "1949-04-01    132.0\n",
       "1949-05-01    129.0\n",
       "              ...  \n",
       "1960-08-01    622.0\n",
       "1960-09-01    606.0\n",
       "1960-10-01    508.0\n",
       "1960-11-01    461.0\n",
       "1960-12-01    390.0\n",
       "Freq: MS, Name: Thousands of Passengers, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Thousands of Passengers\"].shift()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.343456Z",
     "iopub.status.idle": "2022-03-28T04:13:52.343918Z",
     "shell.execute_reply": "2022-03-28T04:13:52.343741Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.343718Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-01-01      NaN\n",
       "1949-02-01      NaN\n",
       "1949-03-01    112.0\n",
       "1949-04-01    118.0\n",
       "1949-05-01    132.0\n",
       "              ...  \n",
       "1960-08-01    535.0\n",
       "1960-09-01    622.0\n",
       "1960-10-01    606.0\n",
       "1960-11-01    508.0\n",
       "1960-12-01    461.0\n",
       "Freq: MS, Name: Thousands of Passengers, Length: 144, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Thousands of Passengers\"].shift().shift()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for Stationarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.345257Z",
     "iopub.status.idle": "2022-03-28T04:13:52.345582Z",
     "shell.execute_reply": "2022-03-28T04:13:52.345441Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.345423Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P Value:  0.991880243437641\n",
      "Weak evidence aganist Null Hypothesis. So, accept Null Hypothesis and conclude data is non-stationary.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "def adf_test(df):\n",
    "    result=adfuller(df)\n",
    "    print(\"P Value: \",result[1])\n",
    "    if result[1]<=0.05:\n",
    "        print(\"Strong evidence aganist Null Hypothesis. So, reject Null Hypothesis and conclude data is stationary.\")\n",
    "        return(True)\n",
    "    else:\n",
    "        print(\"Weak evidence aganist Null Hypothesis. So, accept Null Hypothesis and conclude data is non-stationary.\")\n",
    "        return(False)\n",
    "adf_test(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Automate conversion from non-stationary data to stationary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.346539Z",
     "iopub.status.idle": "2022-03-28T04:13:52.347143Z",
     "shell.execute_reply": "2022-03-28T04:13:52.346986Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.346967Z"
    }
   },
   "outputs": [],
   "source": [
    "def convert_non_stationary_to_stationary(df):\n",
    "    d=0\n",
    "    new_df=df\n",
    "    while True:\n",
    "        new_df=new_df-new_df.shift()\n",
    "        new_df.dropna(inplace=True)\n",
    "        d=d+1\n",
    "        if adf_test(new_df):\n",
    "            print(\"d-value is\",d)\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.348240Z",
     "iopub.status.idle": "2022-03-28T04:13:52.349010Z",
     "shell.execute_reply": "2022-03-28T04:13:52.348838Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.348812Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P Value:  0.0542132902838255\n",
      "Weak evidence aganist Null Hypothesis. So, accept Null Hypothesis and conclude data is non-stationary.\n",
      "P Value:  2.7328918500142407e-29\n",
      "Strong evidence aganist Null Hypothesis. So, reject Null Hypothesis and conclude data is stationary.\n",
      "d-value is 2\n"
     ]
    }
   ],
   "source": [
    "convert_non_stationary_to_stationary(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into train and test datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.349944Z",
     "iopub.status.idle": "2022-03-28T04:13:52.350275Z",
     "shell.execute_reply": "2022-03-28T04:13:52.350138Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.350116Z"
    }
   },
   "outputs": [],
   "source": [
    "train = df.iloc[:len(df)-30]\n",
    "test = df.iloc[len(df)-30:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auto ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.351793Z",
     "iopub.status.idle": "2022-03-28T04:13:52.352401Z",
     "shell.execute_reply": "2022-03-28T04:13:52.352175Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.352158Z"
    }
   },
   "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>144</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(2, 1, 1)x(0, 1, [], 12)</td> <th>  Log Likelihood     </th> <td>-504.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Mon, 28 Mar 2022</td>         <th>  AIC                </th> <td>1017.847</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>10:19:59</td>             <th>  BIC                </th> <td>1029.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                         <td>0</td>                <th>  HQIC               </th> <td>1022.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                             <td> - 144</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>    0.5960</td> <td>    0.085</td> <td>    6.987</td> <td> 0.000</td> <td>    0.429</td> <td>    0.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.2143</td> <td>    0.091</td> <td>    2.343</td> <td> 0.019</td> <td>    0.035</td> <td>    0.394</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.9819</td> <td>    0.038</td> <td>  -25.600</td> <td> 0.000</td> <td>   -1.057</td> <td>   -0.907</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>  129.3144</td> <td>   14.557</td> <td>    8.883</td> <td> 0.000</td> <td>  100.784</td> <td>  157.845</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>7.68</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.98</td> <th>  Prob(JB):          </th> <td>0.02</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.33</td> <th>  Skew:              </th> <td>-0.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.01</td> <th>  Kurtosis:          </th> <td>4.19</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:                  144\n",
       "Model:             SARIMAX(2, 1, 1)x(0, 1, [], 12)   Log Likelihood                -504.923\n",
       "Date:                             Mon, 28 Mar 2022   AIC                           1017.847\n",
       "Time:                                     10:19:59   BIC                           1029.348\n",
       "Sample:                                          0   HQIC                          1022.520\n",
       "                                             - 144                                         \n",
       "Covariance Type:                               opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.5960      0.085      6.987      0.000       0.429       0.763\n",
       "ar.L2          0.2143      0.091      2.343      0.019       0.035       0.394\n",
       "ma.L1         -0.9819      0.038    -25.600      0.000      -1.057      -0.907\n",
       "sigma2       129.3144     14.557      8.883      0.000     100.784     157.845\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 7.68\n",
       "Prob(Q):                              0.98   Prob(JB):                         0.02\n",
       "Heteroskedasticity (H):               2.33   Skew:                            -0.01\n",
       "Prob(H) (two-sided):                  0.01   Kurtosis:                         4.19\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
    "from pmdarima import auto_arima\n",
    "auto_arima(df['Thousands of Passengers'],seasonal=True,m=12).summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model with train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.353229Z",
     "iopub.status.idle": "2022-03-28T04:13:52.353695Z",
     "shell.execute_reply": "2022-03-28T04:13:52.353545Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.353480Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Thousands of Passengers</td>     <th>  No. Observations:  </th>    <td>114</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(2, 1, 1)x(0, 1, [], 12)</td> <th>  Log Likelihood     </th> <td>-370.879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Mon, 28 Mar 2022</td>         <th>  AIC                </th>  <td>749.759</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>10:19:59</td>             <th>  BIC                </th>  <td>760.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>01-01-1949</td>            <th>  HQIC               </th>  <td>753.993</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 06-01-1958</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>   -0.6015</td> <td>    0.807</td> <td>   -0.745</td> <td> 0.456</td> <td>   -2.183</td> <td>    0.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.0129</td> <td>    0.241</td> <td>    0.054</td> <td> 0.957</td> <td>   -0.460</td> <td>    0.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>    0.4168</td> <td>    0.800</td> <td>    0.521</td> <td> 0.602</td> <td>   -1.151</td> <td>    1.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>   90.5207</td> <td>   13.275</td> <td>    6.819</td> <td> 0.000</td> <td>   64.502</td> <td>  116.539</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>2.42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>1.00</td> <th>  Prob(JB):          </th> <td>0.30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>0.96</td> <th>  Skew:              </th> <td>0.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.89</td> <th>  Kurtosis:          </th> <td>2.91</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:             Thousands of Passengers   No. Observations:                  114\n",
       "Model:             SARIMAX(2, 1, 1)x(0, 1, [], 12)   Log Likelihood                -370.879\n",
       "Date:                             Mon, 28 Mar 2022   AIC                            749.759\n",
       "Time:                                     10:19:59   BIC                            760.219\n",
       "Sample:                                 01-01-1949   HQIC                           753.993\n",
       "                                      - 06-01-1958                                         \n",
       "Covariance Type:                               opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.6015      0.807     -0.745      0.456      -2.183       0.980\n",
       "ar.L2          0.0129      0.241      0.054      0.957      -0.460       0.486\n",
       "ma.L1          0.4168      0.800      0.521      0.602      -1.151       1.985\n",
       "sigma2        90.5207     13.275      6.819      0.000      64.502     116.539\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 2.42\n",
       "Prob(Q):                              1.00   Prob(JB):                         0.30\n",
       "Heteroskedasticity (H):               0.96   Skew:                             0.38\n",
       "Prob(H) (two-sided):                  0.89   Kurtosis:                         2.91\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SARIMAX(train['Thousands of Passengers'],order=(2, 1, 1),seasonal_order=(0, 1, [], 12))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the model with test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.354591Z",
     "iopub.status.idle": "2022-03-28T04:13:52.355054Z",
     "shell.execute_reply": "2022-03-28T04:13:52.354848Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.354832Z"
    }
   },
   "outputs": [],
   "source": [
    "start=len(train)\n",
    "end=len(train)+len(test)-1\n",
    "predicted_values = results.predict(start=start, end=end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-28T04:13:52.359042Z",
     "iopub.status.busy": "2022-03-28T04:13:52.358805Z",
     "iopub.status.idle": "2022-03-28T04:13:52.372699Z",
     "shell.execute_reply": "2022-03-28T04:13:52.371501Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.359004Z"
    }
   },
   "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 = test['Thousands of Passengers'].plot(figsize=(12,5))\n",
    "predicted_values.plot()\n",
    "plt.legend()\n",
    "ax.autoscale(axis='x',tight=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.374107Z",
     "iopub.status.idle": "2022-03-28T04:13:52.374732Z",
     "shell.execute_reply": "2022-03-28T04:13:52.374552Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.374524Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error : 2790.7202146416457\n",
      "root_mean_squared_error : 52.82726771887456\n",
      "mean_absolute_error : 44.115291535735786\n",
      "mean_absolute_percentage_error : 0.094358307935176\n"
     ]
    }
   ],
   "source": [
    "import sklearn as sk\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "print(\"mean_squared_error :\",mean_squared_error(test['Thousands of Passengers'],predicted_values ))\n",
    "print(\"root_mean_squared_error :\",mean_squared_error(test['Thousands of Passengers'],predicted_values, squared=False))\n",
    "print(\"mean_absolute_error :\",mean_absolute_error(test['Thousands of Passengers'],predicted_values))\n",
    "print(\"mean_absolute_percentage_error :\",mean_absolute_percentage_error(test['Thousands of Passengers'],predicted_values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrain the model with entire dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.375807Z",
     "iopub.status.idle": "2022-03-28T04:13:52.376495Z",
     "shell.execute_reply": "2022-03-28T04:13:52.376286Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.376260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Thousands of Passengers</td>     <th>  No. Observations:  </th>    <td>144</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(2, 1, 1)x(0, 1, [], 12)</td> <th>  Log Likelihood     </th> <td>-504.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Mon, 28 Mar 2022</td>         <th>  AIC                </th> <td>1017.847</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>10:20:00</td>             <th>  BIC                </th> <td>1029.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                    <td>01-01-1949</td>            <th>  HQIC               </th> <td>1022.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                          <td>- 12-01-1960</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>    0.5960</td> <td>    0.085</td> <td>    6.987</td> <td> 0.000</td> <td>    0.429</td> <td>    0.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.2143</td> <td>    0.091</td> <td>    2.343</td> <td> 0.019</td> <td>    0.035</td> <td>    0.394</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.9819</td> <td>    0.038</td> <td>  -25.600</td> <td> 0.000</td> <td>   -1.057</td> <td>   -0.907</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>  129.3144</td> <td>   14.557</td> <td>    8.883</td> <td> 0.000</td> <td>  100.784</td> <td>  157.845</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>7.68</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.98</td> <th>  Prob(JB):          </th> <td>0.02</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.33</td> <th>  Skew:              </th> <td>-0.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.01</td> <th>  Kurtosis:          </th> <td>4.19</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:             Thousands of Passengers   No. Observations:                  144\n",
       "Model:             SARIMAX(2, 1, 1)x(0, 1, [], 12)   Log Likelihood                -504.923\n",
       "Date:                             Mon, 28 Mar 2022   AIC                           1017.847\n",
       "Time:                                     10:20:00   BIC                           1029.348\n",
       "Sample:                                 01-01-1949   HQIC                          1022.520\n",
       "                                      - 12-01-1960                                         \n",
       "Covariance Type:                               opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.5960      0.085      6.987      0.000       0.429       0.763\n",
       "ar.L2          0.2143      0.091      2.343      0.019       0.035       0.394\n",
       "ma.L1         -0.9819      0.038    -25.600      0.000      -1.057      -0.907\n",
       "sigma2       129.3144     14.557      8.883      0.000     100.784     157.845\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                 7.68\n",
       "Prob(Q):                              0.98   Prob(JB):                         0.02\n",
       "Heteroskedasticity (H):               2.33   Skew:                            -0.01\n",
       "Prob(H) (two-sided):                  0.01   Kurtosis:                         4.19\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = SARIMAX(df['Thousands of Passengers'],order=(2, 1, 1),seasonal_order=(0, 1, [], 12))\n",
    "results = model.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecast the Future...😎"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2022-03-28T04:13:52.377417Z",
     "iopub.status.idle": "2022-03-28T04:13:52.377872Z",
     "shell.execute_reply": "2022-03-28T04:13:52.377708Z",
     "shell.execute_reply.started": "2022-03-28T04:13:52.377683Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_values = results.predict(start=len(df), end=len(df)+30)\n",
    "df['Thousands of Passengers'].plot(figsize=(12,6))\n",
    "predicted_values.plot()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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