RAINFALL_PREDICTION.ipynb

{
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   "source": [
    "# ARIMA and Seasonal ARIMA\n",
    "## ARIMA (AutoRegressive Integrated Moving Averages)\n",
    "An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends.<br>\n",
    "<li>Autoregression (AR): refers to a model that shows a changing variable that regresses on its own lagged, or prior, values</li>\n",
    "<li>Integrated (I): represents the differencing of raw observations to allow for the time series to become stationary (i.e., data values are replaced by the difference between the data values and the previous values).</li>\n",
    "<li>Moving average (MA):  incorporates the dependency between an observation and a residual error from a moving average model applied to lagged observations.</li>\n",
    "\n",
    "## SARIMA (Seasonal AutoRegressive Integrated Moving Average)\n",
    "Seasonal Autoregressive Integrated Moving Average, SARIMA or Seasonal ARIMA, is an extension of ARIMA that explicitly supports univariate time series data with a seasonal component.\n",
    "\n",
    "It adds three new hyperparameters to specify the autoregression (AR), differencing (I) and moving average (MA) for the seasonal component of the series, as well as an additional parameter for the period of the seasonality.\n",
    "\n",
    "The general process for SARIMA models is the following:\n",
    "<li>Clean the dataset</li>\n",
    "<li>Visualize the time series\n",
    "<li>Stationarize the series\n",
    "<li>Plot Autocorrelation and Partial Autocorrelation charts and find optimal parameters\n",
    "<li>Build the SARIMA model\n",
    "<li>Make prediction\n",
    "    \n",
    "Let's go through these steps!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7a335b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c15540",
   "metadata": {},
   "source": [
    "### Importing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dbb6694b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d0bf679",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>states</th>\n",
       "      <th>districts</th>\n",
       "      <th>variable</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-01-2010</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-02-2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-03-2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-04-2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-05-2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      states districts        variable         Unit        Date  Value\n",
       "0  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-01-2010    0.3\n",
       "1  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-02-2010    0.0\n",
       "2  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-03-2010    0.0\n",
       "3  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-04-2010    0.0\n",
       "4  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-05-2010    0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1d492493",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>states</th>\n",
       "      <th>districts</th>\n",
       "      <th>variable</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-08-2020</td>\n",
       "      <td>407.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-09-2020</td>\n",
       "      <td>121.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-10-2020</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-11-2020</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>Udaipur</td>\n",
       "      <td>Rainfall (mm.)</td>\n",
       "      <td>Millimetres</td>\n",
       "      <td>01-12-2020</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        states districts        variable         Unit        Date  Value\n",
       "127  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-08-2020  407.0\n",
       "128  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-09-2020  121.9\n",
       "129  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-10-2020    7.6\n",
       "130  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-11-2020    0.2\n",
       "131  Rajasthan   Udaipur  Rainfall (mm.)  Millimetres  01-12-2020    0.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7780e867",
   "metadata": {},
   "source": [
    "### Cleaning dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0e55e36b",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2010</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02-2010</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-03-2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-04-2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-05-2010</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         Date  Value\n",
       "0  01-01-2010    0.3\n",
       "1  01-02-2010    0.0\n",
       "2  01-03-2010    0.0\n",
       "3  01-04-2010    0.0\n",
       "4  01-05-2010    0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.drop(df.columns[[0, 1, 2, 3]], axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3538a174",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2010</td>\n",
       "      <td>0.3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02-2010</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>01-03-2010</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-04-2010</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>01-05-2010</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "         Date  Value\n",
       "0  01-01-2010    0.3\n",
       "1  01-02-2010    0.0\n",
       "2  01-03-2010    0.0\n",
       "3  01-04-2010    0.0\n",
       "4  01-05-2010    0.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=[\"Date\",\"Value\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ef2f58cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date']=pd.to_datetime(df['Date'],  format='%d-%m-%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eb91a11d",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-03-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-04-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-05-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Date  Value\n",
       "0 2010-01-01    0.3\n",
       "1 2010-02-01    0.0\n",
       "2 2010-03-01    0.0\n",
       "3 2010-04-01    0.0\n",
       "4 2010-05-01    0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "121a3d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db5c9e07",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "            Value\n",
       "Date             \n",
       "2010-01-01    0.3\n",
       "2010-02-01    0.0\n",
       "2010-03-01    0.0\n",
       "2010-04-01    0.0\n",
       "2010-05-01    0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "809a8a59",
   "metadata": {},
   "source": [
    "### Visualize the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "342e91f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c23891e",
   "metadata": {},
   "source": [
    "### Stationarize the series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "438e23bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Testing For Stationarity\n",
    "\n",
    "from statsmodels.tsa.stattools import adfuller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "29161f82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ho: It is non stationary\n",
    "#H1: It is stationary\n",
    "\n",
    "def adfuller_test(values):\n",
    "    result=adfuller(values)\n",
    "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
    "    for value,label in zip(result,labels):\n",
    "        print(label+' : '+str(value) )\n",
    "    if result[1] <= 0.05:\n",
    "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d197379e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -2.9192061653136365\n",
      "p-value : 0.04315973392525896\n",
      "#Lags Used : 11\n",
      "Number of Observations Used : 120\n",
      "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "adfuller_test(df['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a19dcd26",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Differencing\n",
    "df['First Difference'] = df['Value'] - df['Value'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bbf026c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-01-01      NaN\n",
       "2010-02-01      0.3\n",
       "2010-03-01      0.0\n",
       "2010-04-01      0.0\n",
       "2010-05-01      0.0\n",
       "              ...  \n",
       "2020-08-01    128.4\n",
       "2020-09-01    407.0\n",
       "2020-10-01    121.9\n",
       "2020-11-01      7.6\n",
       "2020-12-01      0.2\n",
       "Name: Value, Length: 132, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Value'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c4d89d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Seasonal First Difference']=df['Value']-df['Value'].shift(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3967d296",
   "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>Value</th>\n",
       "      <th>First Difference</th>\n",
       "      <th>Seasonal First Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>0.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>49.6</td>\n",
       "      <td>49.6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>280.8</td>\n",
       "      <td>231.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>343.8</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>104.1</td>\n",
       "      <td>-239.7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-104.1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>94.2</td>\n",
       "      <td>94.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>0.9</td>\n",
       "      <td>-93.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>-0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>39.7</td>\n",
       "      <td>39.7</td>\n",
       "      <td>-9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>232.1</td>\n",
       "      <td>192.4</td>\n",
       "      <td>-48.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>376.8</td>\n",
       "      <td>144.7</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value  First Difference  Seasonal First Difference\n",
       "Date                                                          \n",
       "2010-01-01    0.3               NaN                        NaN\n",
       "2010-02-01    0.0              -0.3                        NaN\n",
       "2010-03-01    0.0               0.0                        NaN\n",
       "2010-04-01    0.0               0.0                        NaN\n",
       "2010-05-01    0.0               0.0                        NaN\n",
       "2010-06-01   49.6              49.6                        NaN\n",
       "2010-07-01  280.8             231.2                        NaN\n",
       "2010-08-01  343.8              63.0                        NaN\n",
       "2010-09-01  104.1            -239.7                        NaN\n",
       "2010-10-01    0.0            -104.1                        NaN\n",
       "2010-11-01   94.2              94.2                        NaN\n",
       "2010-12-01    0.9             -93.3                        NaN\n",
       "2011-01-01    0.0              -0.9                       -0.3\n",
       "2011-02-01    0.0               0.0                        0.0\n",
       "2011-03-01    0.0               0.0                        0.0\n",
       "2011-04-01    0.0               0.0                        0.0\n",
       "2011-05-01    0.0               0.0                        0.0\n",
       "2011-06-01   39.7              39.7                       -9.9\n",
       "2011-07-01  232.1             192.4                      -48.7\n",
       "2011-08-01  376.8             144.7                       33.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f0995fb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -10.402375786755307\n",
      "p-value : 1.8947515284875764e-18\n",
      "#Lags Used : 0\n",
      "Number of Observations Used : 119\n",
      "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "## Again test dickey fuller test\n",
    "adfuller_test(df['Seasonal First Difference'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bcf7995e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Seasonal First Difference'].plot()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "100e8323",
   "metadata": {},
   "source": [
    "## Auto Regressive Model\n",
    "An autoregressive model of order p can be written as\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db67caab",
   "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": [
    "from pandas.plotting import autocorrelation_plot\n",
    "autocorrelation_plot(df['Value'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ebf8f8",
   "metadata": {},
   "source": [
    "# Final Thoughts on Autocorrelation and Partial Autocorrelation\n",
    "<li>Identification of an AR model is often best done with the PACF.<br>\n",
    "For an AR model, the theoretical PACF “shuts off” past the order of the model. The phrase “shuts off” means that in theory the partial autocorrelations are equal to 0 beyond that point. Put another way, the number of non-zero partial autocorrelations gives the order of the AR model. By the “order of the model” we mean the most extreme lag of x that is used as a predictor.\n",
    "</li>\n",
    "<li>Identification of an MA model is often best done with the ACF rather than the PACF.<br>\n",
    "For an MA model, the theoretical PACF does not shut off, but instead tapers toward 0 in some manner. A clearer pattern for an MA model is in the ACF. The ACF will have non-zero autocorrelations only at lags involved in the model.\n",
    "</li>\n",
    "p,d,q where <b>p</b> is AR model lags, <b>d</b> is number of times of differencing and <b>q</b> is MA lags "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0bd5256a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ebf16c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "120891c3",
   "metadata": {},
   "source": [
    "### ARIMA prediction plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f85eb35c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# arima pdq order\n",
    "#p=1, d=1, q=0 or 1\n",
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6774da78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "model=ARIMA(df['Value'],order=(1,1,0))\n",
    "model_fit=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ac32b622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARIMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>D.Value</td>     <th>  No. Observations:  </th>    <td>131</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>          <td>ARIMA(1, 1, 0)</td>  <th>  Log Likelihood     </th> <td>-805.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>  <td>113.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 03 Nov 2021</td> <th>  AIC                </th> <td>1617.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>09:32:10</td>     <th>  BIC                </th> <td>1625.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>02-01-2010</td>    <th>  HQIC               </th> <td>1620.615</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 12-01-2020</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>    0.0016</td> <td>    9.334</td> <td>    0.000</td> <td> 1.000</td> <td>  -18.292</td> <td>   18.295</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.D.Value</th> <td>   -0.0614</td> <td>    0.087</td> <td>   -0.706</td> <td> 0.480</td> <td>   -0.232</td> <td>    0.109</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>         -16.2978</td> <td>          +0.0000j</td> <td>          16.2978</td> <td>           0.5000</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                             ARIMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                D.Value   No. Observations:                  131\n",
       "Model:                 ARIMA(1, 1, 0)   Log Likelihood                -805.555\n",
       "Method:                       css-mle   S.D. of innovations            113.332\n",
       "Date:                Wed, 03 Nov 2021   AIC                           1617.110\n",
       "Time:                        09:32:10   BIC                           1625.736\n",
       "Sample:                    02-01-2010   HQIC                          1620.615\n",
       "                         - 12-01-2020                                         \n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "const             0.0016      9.334      0.000      1.000     -18.292      18.295\n",
       "ar.L1.D.Value    -0.0614      0.087     -0.706      0.480      -0.232       0.109\n",
       "                                    Roots                                    \n",
       "=============================================================================\n",
       "                  Real          Imaginary           Modulus         Frequency\n",
       "-----------------------------------------------------------------------------\n",
       "AR.1          -16.2978           +0.0000j           16.2978            0.5000\n",
       "-----------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_fit.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "95901d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['forecast']=model_fit.predict(start=118,end=130,dynamic=True)\n",
    "df[['Value','forecast']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169d5b42",
   "metadata": {},
   "source": [
    "### SARIMA prediction plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0ec94e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=sm.tsa.statespace.SARIMAX(df['Value'],order=(1, 1, 1),seasonal_order=(1,1,1,12))\n",
    "results=model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "270f01c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['forecast']=results.predict(start=118,end=130,dynamic=True)\n",
    "df[['Value','forecast']].plot(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c4b428ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.tseries.offsets import DateOffset\n",
    "future_dates=[df.index[-1]+ DateOffset(months=x)for x in range(0,24)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "89b43086",
   "metadata": {},
   "outputs": [],
   "source": [
    "future_datest_df=pd.DataFrame(index=future_dates[1:],columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "83bc3983",
   "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>Value</th>\n",
       "      <th>First Difference</th>\n",
       "      <th>Seasonal First Difference</th>\n",
       "      <th>forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Value First Difference Seasonal First Difference forecast\n",
       "2022-07-01   NaN              NaN                       NaN      NaN\n",
       "2022-08-01   NaN              NaN                       NaN      NaN\n",
       "2022-09-01   NaN              NaN                       NaN      NaN\n",
       "2022-10-01   NaN              NaN                       NaN      NaN\n",
       "2022-11-01   NaN              NaN                       NaN      NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_datest_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "42e68d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "future_df=pd.concat([df,future_datest_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2d20502c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "future_df['forecast'] = results.predict(start = 131, end = 150, dynamic= True)  \n",
    "future_df[['Value', 'forecast']].plot(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a6c6cea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}