decomposing time series into level, trend, seasonality, and noise.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1- Exploring Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>SUBDIVISION</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>JAN</th>\n",
       "      <th>FEB</th>\n",
       "      <th>MAR</th>\n",
       "      <th>APR</th>\n",
       "      <th>MAY</th>\n",
       "      <th>JUN</th>\n",
       "      <th>JUL</th>\n",
       "      <th>AUG</th>\n",
       "      <th>SEP</th>\n",
       "      <th>OCT</th>\n",
       "      <th>NOV</th>\n",
       "      <th>DEC</th>\n",
       "      <th>ANNUAL</th>\n",
       "      <th>Jan-Feb</th>\n",
       "      <th>Mar-May</th>\n",
       "      <th>Jun-Sep</th>\n",
       "      <th>Oct-Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1901</td>\n",
       "      <td>49.2</td>\n",
       "      <td>87.1</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>528.8</td>\n",
       "      <td>517.5</td>\n",
       "      <td>365.1</td>\n",
       "      <td>481.1</td>\n",
       "      <td>332.6</td>\n",
       "      <td>388.5</td>\n",
       "      <td>558.2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>3373.2</td>\n",
       "      <td>136.3</td>\n",
       "      <td>560.3</td>\n",
       "      <td>1696.3</td>\n",
       "      <td>980.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1902</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>446.1</td>\n",
       "      <td>537.1</td>\n",
       "      <td>228.9</td>\n",
       "      <td>753.7</td>\n",
       "      <td>666.2</td>\n",
       "      <td>197.2</td>\n",
       "      <td>359.0</td>\n",
       "      <td>160.5</td>\n",
       "      <td>3520.7</td>\n",
       "      <td>159.8</td>\n",
       "      <td>458.3</td>\n",
       "      <td>2185.9</td>\n",
       "      <td>716.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1903</td>\n",
       "      <td>12.7</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>235.1</td>\n",
       "      <td>479.9</td>\n",
       "      <td>728.4</td>\n",
       "      <td>326.7</td>\n",
       "      <td>339.0</td>\n",
       "      <td>181.2</td>\n",
       "      <td>284.4</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2957.4</td>\n",
       "      <td>156.7</td>\n",
       "      <td>236.1</td>\n",
       "      <td>1874.0</td>\n",
       "      <td>690.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1904</td>\n",
       "      <td>9.4</td>\n",
       "      <td>14.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202.4</td>\n",
       "      <td>304.5</td>\n",
       "      <td>495.1</td>\n",
       "      <td>502.0</td>\n",
       "      <td>160.1</td>\n",
       "      <td>820.4</td>\n",
       "      <td>222.2</td>\n",
       "      <td>308.7</td>\n",
       "      <td>40.1</td>\n",
       "      <td>3079.6</td>\n",
       "      <td>24.1</td>\n",
       "      <td>506.9</td>\n",
       "      <td>1977.6</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ANDAMAN &amp; NICOBAR ISLANDS</td>\n",
       "      <td>1905</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>26.9</td>\n",
       "      <td>279.5</td>\n",
       "      <td>628.7</td>\n",
       "      <td>368.7</td>\n",
       "      <td>330.5</td>\n",
       "      <td>297.0</td>\n",
       "      <td>260.7</td>\n",
       "      <td>25.4</td>\n",
       "      <td>344.7</td>\n",
       "      <td>2566.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>309.7</td>\n",
       "      <td>1624.9</td>\n",
       "      <td>630.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SUBDIVISION  YEAR   JAN    FEB   MAR    APR    MAY    JUN  \\\n",
       "0  ANDAMAN & NICOBAR ISLANDS  1901  49.2   87.1  29.2    2.3  528.8  517.5   \n",
       "1  ANDAMAN & NICOBAR ISLANDS  1902   0.0  159.8  12.2    0.0  446.1  537.1   \n",
       "2  ANDAMAN & NICOBAR ISLANDS  1903  12.7  144.0   0.0    1.0  235.1  479.9   \n",
       "3  ANDAMAN & NICOBAR ISLANDS  1904   9.4   14.7   0.0  202.4  304.5  495.1   \n",
       "4  ANDAMAN & NICOBAR ISLANDS  1905   1.3    0.0   3.3   26.9  279.5  628.7   \n",
       "\n",
       "     JUL    AUG    SEP    OCT    NOV    DEC  ANNUAL  Jan-Feb  Mar-May  \\\n",
       "0  365.1  481.1  332.6  388.5  558.2   33.6  3373.2    136.3    560.3   \n",
       "1  228.9  753.7  666.2  197.2  359.0  160.5  3520.7    159.8    458.3   \n",
       "2  728.4  326.7  339.0  181.2  284.4  225.0  2957.4    156.7    236.1   \n",
       "3  502.0  160.1  820.4  222.2  308.7   40.1  3079.6     24.1    506.9   \n",
       "4  368.7  330.5  297.0  260.7   25.4  344.7  2566.7      1.3    309.7   \n",
       "\n",
       "   Jun-Sep  Oct-Dec  \n",
       "0   1696.3    980.3  \n",
       "1   2185.9    716.7  \n",
       "2   1874.0    690.6  \n",
       "3   1977.6    571.0  \n",
       "4   1624.9    630.8  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading dataset\n",
    "path = 'D:/Datasets/flood dataset - kaggle/flood prediction/datasets_1686_3183_rainfall in india 1901-2015.csv'\n",
    "data = pd.read_csv(path, na_values = ['na', '--'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling NULL with mean of each values of column, because nun values will cause error in the following way\n",
    "data['JAN'].fillna((data['JAN'].mean()), inplace=True)\n",
    "data['FEB'].fillna((data['FEB'].mean()), inplace=True)\n",
    "data['MAR'].fillna((data['MAR'].mean()), inplace=True)\n",
    "data['APR'].fillna((data['APR'].mean()), inplace=True)\n",
    "data['MAY'].fillna((data['MAY'].mean()), inplace=True)\n",
    "data['JUN'].fillna((data['JUN'].mean()), inplace=True)\n",
    "data['JUL'].fillna((data['JUL'].mean()), inplace=True)\n",
    "data['AUG'].fillna((data['AUG'].mean()), inplace=True)\n",
    "data['SEP'].fillna((data['SEP'].mean()), inplace=True)\n",
    "data['OCT'].fillna((data['OCT'].mean()), inplace=True)\n",
    "data['DEC'].fillna((data['DEC'].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To prepare dataset for timeseries forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       level_0 level_1      0\n",
      "0            0     JAN   49.2\n",
      "1            0     FEB   87.1\n",
      "2            0     MAR   29.2\n",
      "3            0     APR    2.3\n",
      "4            0     MAY  528.8\n",
      "...        ...     ...    ...\n",
      "41155     4115     JUN  296.6\n",
      "41156     4115     JUL  257.5\n",
      "41157     4115     AUG  146.4\n",
      "41158     4115     SEP  160.4\n",
      "41159     4115     OCT  165.4\n",
      "\n",
      "[41160 rows x 3 columns]\n",
      "      index        0\n",
      "0         0  1901-01\n",
      "1         1  1901-02\n",
      "2         2  1901-03\n",
      "3         3  1901-04\n",
      "4         4  1901-05\n",
      "...     ...      ...\n",
      "1376   1376  2015-09\n",
      "1377   1377  2015-10\n",
      "1378   1378  2015-11\n",
      "1379   1379  2015-12\n",
      "1380   1380  2016-01\n",
      "\n",
      "[1381 rows x 2 columns]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1901-01</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1901-02</td>\n",
       "      <td>87.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1901-03</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1901-04</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1901-05</td>\n",
       "      <td>528.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     Month  Rainfall\n",
       "0  1901-01      49.2\n",
       "1  1901-02      87.1\n",
       "2  1901-03      29.2\n",
       "3  1901-04       2.3\n",
       "4  1901-05     528.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Month        object\n",
       "Rainfall    float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here, each month has been separated into a column with its value\n",
    "df_month = pd.DataFrame(data[['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL','AUG', 'SEP', 'OCT']])\n",
    "df_month = df_month.T\n",
    "df_month = df_month.unstack(level=0)\n",
    "df_month = df_month.reset_index()\n",
    "print(df_month)\n",
    "\n",
    "# Creation of year/month in a separate column\n",
    "df_time = pd.Series(pd.period_range(start='1901', end='2016', freq='M', periods=None), dtype=str)\n",
    "df_time = df_time.reset_index()\n",
    "print(df_time)\n",
    "\n",
    "# To create a new datafarame which record the values of rainfall in the specific year and moth\n",
    "df = pd.DataFrame({'Month':df_time[0],\n",
    "                  'Rainfall':df_month[0]})\n",
    "display(df.head())\n",
    "display(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Month  Rainfall\n",
      "0 1901-01-01      49.2\n",
      "1 1901-02-01      87.1\n",
      "2 1901-03-01      29.2\n",
      "3 1901-04-01       2.3\n",
      "4 1901-05-01     528.8\n",
      "Month       datetime64[ns]\n",
      "Rainfall           float64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rainfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1901-01-01</th>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901-02-01</th>\n",
       "      <td>87.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901-03-01</th>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901-04-01</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1901-05-01</th>\n",
       "      <td>528.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Rainfall\n",
       "Month               \n",
       "1901-01-01      49.2\n",
       "1901-02-01      87.1\n",
       "1901-03-01      29.2\n",
       "1901-04-01       2.3\n",
       "1901-05-01     528.8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Month values are object, so we should change it as to_datetime pandas format\n",
    "df['Month'] = pd.to_datetime(df['Month'])\n",
    "print(df.head())\n",
    "print(df.dtypes)\n",
    "\n",
    "# Here, we replace the index number with datatime (Month column)\n",
    "df.set_index('Month', inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To plot raining values\n",
    "plt.plot(df['Rainfall'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is going up and coming down so taht's probabley the seasonality is reflecting\n",
    "not stationary that means there are ups and downs over the period shown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Various flavors of ARIMA Model\n",
    "\n",
    "ARIMA: Non-seasonal Autoregressive Integrated Moving Averages\n",
    "\n",
    "SARIMA: Seasonal ARIMA\n",
    "    \n",
    "SARIMAX: Seasonal ARIMA with exogenous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.01, False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To determine is the data stationary or not\n",
    "# Stationary means independ on seasonal data and need ARIMA\n",
    "# Not stationary means depend on seasonal data and need SARIMA \n",
    "from pmdarima.arima import ADFTest\n",
    "adf_test = ADFTest(alpha=0.05)\n",
    "adf_test.should_diff(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pvalue =  6.6207417883846186e-06 if above 0.05, data is not stationary\n"
     ]
    }
   ],
   "source": [
    "# Dickey-Fuller test : this is similart to t-test\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "adf, pvalue, usedlag_, nobs_, critical_values, icbest_ = adfuller(df)\n",
    "print(\"pvalue = \", pvalue, \"if above 0.05, data is not stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Rainfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1901-01</td>\n",
       "      <td>JAN</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1901-02</td>\n",
       "      <td>FEB</td>\n",
       "      <td>87.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1901-03</td>\n",
       "      <td>MAR</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1901-04</td>\n",
       "      <td>APR</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1901-05</td>\n",
       "      <td>MAY</td>\n",
       "      <td>528.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year Month  Rainfall\n",
       "0  1901-01   JAN      49.2\n",
       "1  1901-02   FEB      87.1\n",
       "2  1901-03   MAR      29.2\n",
       "3  1901-04   APR       2.3\n",
       "4  1901-05   MAY     528.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Year         object\n",
       "Month        object\n",
       "Rainfall    float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we add a cloumn called 'year' to see how the number is changing because it make it easy to plot\n",
    "#df['year'] = [d.year for d in df.index]\n",
    "# Here we split each month of year as a separte column\n",
    "#df['month'] = [d.strftime('%B') for d in df.index]\n",
    "#years = df['year'].unique()\n",
    "\n",
    "# To create a new datafarame which record the values of rainfall in the specific year and moth\n",
    "df_new = pd.DataFrame({'Year':df_time[0],\n",
    "                   'Month':df_month['level_1'],\n",
    "                   'Rainfall':df_month[0]})\n",
    "\n",
    "years = df_new['Year'].unique()\n",
    "display(df_new.head())\n",
    "display(df_new.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month', ylabel='Rainfall'>"
      ]
     },
     "execution_count": 10,
     "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": [
    "# Plot yearly and monthly values as boxplot\n",
    "#sns.boxplot(x='Year', y='Rainfall', data=df_new)\n",
    "sns.boxplot(x='Month', y='Rainfall', data=df_new)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see more details about this part, visit the website:\n",
    "    \n",
    "https://machinelearningmastery.com/decompose-time-series-data-trend-seasonality/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract and plot trend, seasonal and residuals\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "decomposed = seasonal_decompose(df_new['Rainfall'], \n",
    "                                period = 1,\n",
    "                               model='additive')\n",
    "\n",
    "#Additive time series:\n",
    "#Value = Base Level + Trend + Seasonality + Error\n",
    "#Multiplicative Time Series:\n",
    "#Value = Base Level x Trend x Seasonality x Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0         49.2\n",
      "1         87.1\n",
      "2         29.2\n",
      "3          2.3\n",
      "4        528.8\n",
      "         ...  \n",
      "41155    296.6\n",
      "41156    257.5\n",
      "41157    146.4\n",
      "41158    160.4\n",
      "41159    165.4\n",
      "Name: trend, Length: 41160, dtype: float64\n",
      "0        0.0\n",
      "1        0.0\n",
      "2        0.0\n",
      "3        0.0\n",
      "4        0.0\n",
      "        ... \n",
      "41155    0.0\n",
      "41156    0.0\n",
      "41157    0.0\n",
      "41158    0.0\n",
      "41159    0.0\n",
      "Name: seasonal, Length: 41160, dtype: float64\n",
      "0        0.0\n",
      "1        0.0\n",
      "2        0.0\n",
      "3        0.0\n",
      "4        0.0\n",
      "        ... \n",
      "41155    0.0\n",
      "41156    0.0\n",
      "41157    0.0\n",
      "41158    0.0\n",
      "41159    0.0\n",
      "Name: resid, Length: 41160, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "trend = decomposed.trend\n",
    "seasonal = decomposed.seasonal   #decomposed give you seasonal information\n",
    "residual = decomposed.resid\n",
    "\n",
    "print(trend)\n",
    "print(seasonal)\n",
    "print(residual)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(411)\n",
    "plt.plot(df_new['Rainfall'], label='Original', color='blue')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(412)\n",
    "plt.plot(trend, label='Trend', color='blue')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(413)\n",
    "plt.plot(seasonal, label='Seasonal', color='blue')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(414)\n",
    "plt.plot(residual, label='Residual', color='blue')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VHD\\anaconda3\\envs\\SI\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:572: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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xuc7HxuMbM0ck0o7dey6pJH5NuQ7ItcNIpBI6Z9eNZKtEyNfkr9WJDgey2VTlyE2eyBaINhEV3bM4/E507zmT6ybYtw6/xnjwKnJOPBNKadhAFDXwafhllIoFLFbdq0riGjYRBdUabuPcHIwxmYrwWnLKLyvczjk4gwDIcXIw5/hmv1QvozeaOM/RMGpoRk7PleXOlg5Lm27XdDSZYmMwDqIbaZNFKolVE8mf3efTzY6Us9S8v8lQUbPWHaFZKaJaKmKlySe9mWDfOvxIw3dvraCcMpuk0x2FjqRSKqBZKTozUjUmlVheYpAL1AIPtXGGJGM81wBIVsola7Br+PHoju3eD0Nnp2y7FgFs1fB5SEl07/kcaSu+MTHKRK2erFCql4vybANTXmAljOx5CMRadxhuyJyVTybYtw6/Vgrq8Fk0fHnjF2slEHFo+JFNACyhveqUqSIbjpAxnmAEpJPmzDUo267XUx1mio+T4zBbOxbdcTLnaBPhqVJS128pnrh0vU9hdBeXX9wjW0DO90O1MgrkHtmq/vrLjTgb5ymhPBx878ONClskcniLw/cMnw2lYgHlIjk5/OlUbAnrCwViOW0bl3SAQHN2ZmQRIwF4WG68fBQAy0GheK4B4PruEcNV/x+O3ZuyKeYIgNHp8eeE1jrRZh8mLh0dVFwmimy6RiLRgcNCgYITvG7jVP31tybseaq+1CbPFTGudaIolLOaygT71uED7o857AzHmArgUD169O8SQ5IxnhcA5ITi0NuXgskkbbqHoa0Yy5M23U8ax5OW0nYZneHEqZFU/OSyshn/vQ3CzT50JEyyRi8iEFH+hm+zjyIRpvtUr6DMlBOa3ZhZSEl363ziyN+oFgjqmh5uVlhq5te6IxyO3SeOnJAp9rXDd30IitosGpXI4TcrJXSHbodFOoMxFmuRw1+sldAZuDd5W45vIgyVKq2ePNCi2lSssEhP2yUdwE0mi0sF8f+7jHVjIDf7UNKZJ8N3lnSG4WbfrBRRLhKL9FQtFcJc2HLTPRJLcs5shQWKOTd5yJO0Fc0njkdcbmX4fIcOTbCvHX7N1eEHf6tKPAGZDHaRCoQQ6I0moRMNxzl2c/ibgwkWatHG1KgWnQ/KtAPmSCT7rC81yhiMp05R0/bkclCW6hA5rHVnpKeGO3NuzzgSDvlFtcXevjHxMXwiYorutsqOqsmfC1Sly0pM1nDVxrdHDe6kRI0p1PCbZYwmApsO/a5UhVKo4atIbIdlnX3t8OtlN+ccOvxK5PBr5YJT5c9oIjAVM5uIo/QEyPLTWZvD8RQTh14lG/3ZSKQc/N6BOffHqBQLYXL5ULBJtXv2i0mN51Btq8N3YbmRpCFtLVTlOF2a3IVtsYPxNStFlArEUpK7EpPzDjMwZ9WbR4GjY2arO0SlWEBDRQ2cEWNMemv1Rk49erZVpzFEjPGy6fj/d7pSh8XhE9HDRPQCEV0kok+mvOdHieg5InqWiH6L43PzUKsU0XNwzsqxx9m4q3NWm0itHN9E3DYmQMpPsw4fcDuH0BtNwsW51abLNZ1s2UCVzQHDxqzG2gwkOBfpbTPo8bIYbEhE5CwRxqtplE2OZwW3u6OwzBUAS8uG7QyfJ7m+HIsYZa95fploMhVOPXpUclqxccX0XaKRWZmI8xyCCZwdPhEVAXwOwAcBnAbwKBGdnnnPvQA+BeD7hBD3A/gp18/VQb1cQN/FOQ+TnbPLou8nRA31ivsD13uzjpThATC94WTLd68ztKuY3ZhUfoQjElNj5eij1E3I39Qrjpv9cOvGBMjIoePYGjt+VgSQkQNHOXLc5mKtFG6CtojnLwC58bk+fziU8+pb2bjLRrKNjQdtIFwSt7MyEeepYBNwMPz3AbgohHhZCDEE8DiAR2be8w8AfE4IsQYAQog3GD43F3VHbbyfpOG7ykTDZJujiXDK2PdHE1Rn8gLxz7NBb1Ymqrifbdi+MUmbLvmG/nACIqAanL1QDtXFZiTnzUR3jBuT+rfLPRpNphhPBRqzeSZHibAzmISREuD+3QF5P5Q0BkT3yTUn1KjIA1dAFJG56O0qMlQ5sYWqdPhdh8KKjTBi5D3EaQoOh38CwKXYz5eD38VxH4D7iOi/ENHTRPRwkiEi+jgRnSWis6urq84Dc11MSRq+a9JWbUBbF30heM3O4U+nAoPxlF3Smc0LcG0isw5P/d7FZr1cDKWCWonHZnx88t8Ft3s/hyKA5DyTu3NOkt56o4lTpcq2zZ7h3neHYzSrWzcmgCe6U/OIJbKduU9cT+QzxU4lbUsA7gXwAwAeBfCviGh59k1CiMeEEGeEEGeOHTvm/KGurERNGnXjQ5sMYX2Sc7a1qzaRRJucbJxtE9nKml1tzkYihQKhWpqPc3aRCLsJkg5X6XD8PjUYGP726K4EIYCBw3mJ7myeiUl2TLLpGt1VSwUUCgGBYIhs+zP3qVggVEoFdEfuzwMwAYfDvwLgrtjPJ4PfxXEZwBNCiJEQ4rsAXoTcAOaKmqM2nsSequUiBuOpdRVAWlgP2Du9pEVfY2AQsxo+i94+TMk1OG2i0y3jVHY58jdJLNfV5tZr6lY+m0YgOEqHt35390eGyqghYuNzkR0ZiE5/Zo6G896hCCCxxLvsNkdtwOHwvw7gXiK6h4gqAD4C4ImZ9/weJLsHER2FlHheZvjsTEj25ObwACQmLm2ZTlrSFrCfpEmbCJvezhiJJNnkkF9m5QeAUW8vbd1E95r8kuZIXOSXwXgKIXjnKKDYeORyVKTjRsqmqCWMkzNiDPtyDR18yRzygTZwdvhCiDGATwB4CsDzAL4ohHiWiD5LRB8O3vYUgBtE9ByArwL4n4UQN1w/Ow9Kc7Wd+MlJWzdHmlTqqZyK7SRNGicHe5KONDZOpk0kvjFxyC+zCxRQi8k1uonCemXTtSRV2dlik6PMN76JVIpO8ktS1MCRa+kOx4kEwi3CGW9NWHPYHG3Nh5WK8slsHNGdKiwAVP5mZzX8Uv5b8iGEeBLAkzO/+0zs3wLATwf/7Rjq5SImU4HRRKBSovw/mEF/NEWBgHIxtuhd2XhSCO4oaygnxJm0HU2mGE0EO8PvDxOcM4P8MmuTI2GfOE7uCi3XIoCMnFB/ZnPVHmfKxgS4EohpsqTjuNnfthiVenJU/sxKmYA6cOlGnmYJhOsctcG+PmnrOqFmqz+22LS8UUmLiWOccTuA+8bUT7DJVlEzB/mlNmuzUkTPISGWtIm4H7qbhmPbYpPlvACfjp0kPbnmb8aTKYaTaSLRcd1EuSORRInQ9QxGYhTqtonYYF87fNcJleScXBOsoSNlrH6JEozbq1+cN6bYOMsMLafnIb/MVv4A7pUqSZsIl94+G9b3R+5FAJwRY3Ik4pa0VSXHnHMUkFFDnJRUSwUQ8Wr4gNzwuowbU2jTsRGjKfa1w3fWxhNCO1fnnFTqGdbhWzq9rMofTulJ2bVdoEKIbQvU1SaQvEBrLJsIr8NXNuMRYzifLA8IzuXepySXXWwqx1avbK+Zd80LxKMbIkKj7F75NI85mkwg9mcd/q7AWdYYJzh8hoqaYoFY8wJJyUDFdGx71CQxR/Wz7WY3GG+XNNTPzmF9gkzkevgm6bu7NKRLHKdz/ia5fDT+mimyzorYk6eEPBNH5U+a/MJe9eWu4XPnhGywvx2+q6yRsujdbE5TWZ5zCD7DdGol+4kflqQyLqa0qIFFw2fX25MciYrE3HJCccwjEos2Eb6I0VkmyiAltgn76TQ9YnQ9IDcrEXKciE7S8H3SlhFcSVtum0kT1NUmkOBIXZxzBsPnXPSA/P5OvXRS2BPnCU6AIRk63PochLhN202km8HGXTeR+SSCo++vOpDa3vswYky4T64nbdkT9ilR6C1Xh7+XoS7wwFobnyYyXBebs7XtgLv8EtX2J01SxwNijDp2Us044BbaJpWPhuN0PoOQsjE7bHjx7ptAPLqzv0/xNgBANEdtE4JJGr5ry4JQwy9v//7upCQhYe8sE82Ms1JytDndPkcZupqaYl87/JrjIanBaBKesgttllwPXk22JGwBBvlltLVbZDhWB91ROSBObTyUiWbG2XBwzkkaNhDo7ZOpdQfStDr8+Gca20ys1HB3zknfHeDV8CvFAgoO1S9p98mFQKhrNruJuticTAWG4+3O2VV+SSIQjXLJ+SFFptjXDp+jNDE1ycZY6qnsum4i8byAq815yESpzplhnLPRTXhs3/K0aTejQstJzpt1eAwH+djzTAnXVMkvLrmr+NgUXKK7pBJnLpuzUbj7eYlxwnd3P7luioPh8BkXU9j7xSHBmnT60UV+SdIHI5u8jnReGr71JpLiSEJH6hA5pDFnJ5vMGn5WJGJb8tcbTlBIiBhdNuaIjTPOp5R779KQLnWOMuSEUgnEDiZu97XDr7mG4AkJVtX7xYVBzE4mAKiWC0612Ek2XR6OPtvOdYtNB0eibMThUu6YpDcrm4DdvVd5gUbaJsLpnDlIycx3rzrKjkknzAHHjTmLQDBHoS75m3nMUUC1lUjOCe1kaeb+dviOB68GCTcJcJU1ptsqNYBAG3dgJUk2nSa+WqDbWJ59E6lU5+xQ7piWXHZxpFl5AdtxAnl1+Hy5Btfn76bJji6nl9Pvvf3ho+gwF1+eKe3eNxw2+6S2EvHP8JIOE8pFQrFg1wpA3aTZBCsgNxLOEBxwYzqz/bvjNl3GWSkWUComaJmuCVZG55wqPTm0AsiSs2zHqf5uHgf5UkmJy/mTNInQtQggUSK0S1inzad5SDou8ks/pXyUo9GbKfa1w48STeYMIqn3h4ILK5lL0na8vfIHcF2gyVGDsmnTcjqrXUP8dSObGdUfgF0ZYVpewJWNJ54XCL+7ZZVOVk6IMXcFuEWM3dEYlVIBxcIcCgtS6tvnMUdtCFTaIUaXOWqLfe3wAXvdMe3GhzZtWW7KAnXVxrlZXlLSEogmrU2v9aRukfGfrSSdjNO7tjbVY+dS6/Ad8gKz4ywXCyhZRqFAek7I5fm7qVGoS/VL5iZiO++zE/Z2czS98if+ugnyIluv4TOiVi5YHWhKSzIBcOqrkdSfR32Ovfyy/Xh5ZJNPGwYQJjJd2Pi26g8GSWd7uGzfzjerBQRgyfJS2Kiya+v0usPJtsoX9TluOSHmqCFDyrQ/ZZyu4cvX+e+9UxTqNfz5w3aSZjp8yzA0jeXJcdof7EhjeerwkVX1S5pU4Mh0kqo/OBZTLaFu2tXmrCMJc0JWMlH6fHIp+Utzzo2y/cnQXsYmYl+WmWbT/ql0WRo+wEsgnEhJQvvyLTa9pMMH20maduPV7zgrStTvuDVXl+qXtFyDi6yRJj2p8Nmm33iq5upwqCXtPoU5oT0yn4Bs6c0lccl9riPt6Vv1cvRUOptxznaeBeaTE3JpHjevIgAb7HuHb6u3J3WgVKg6LvpZfVD9jruMzsU5Z0UNgP3Ez7JpI0Fk1XfHXzdBWlsJ9Tku9z6J5cqqEvOkbV7E6FI+mn44kLuayEV62955Vo0TcEywMsovuZKOZ/h8qJeLVsfr03bl0KbFTRokPHtWoVYqYjC2e/JRGntyZTppiTv1uo3NtMofF5ulAqE8Uz7qUgERNfpKjpps7n1apQZg/yCMeUWMqZt9xa36hZ9AjFPOC8hNxKpCK2VjdirLzDjECHiGz4pa2W6BRh0o00sTTRFtIsmlnoD5k4+mU4HBOP0wF2DPdJKck+vET6smAtxY3ixcHseYnbC3O72cK+kwR6EuFVppenutXMRUAEOLhnTd1HtvL73lSZmcBGIehQUcjww1xb53+K5J2yymY4qsUk9bR6qcT6ZNq++f7EidNPzUEkK3SCRpY1J2OXVcwF7WmMt8yrBpKz0JITSkN7toZKckQqf5lEIgXNq05N6nW03SIaKHiegFIrpIRJ/MeN/fISJBRGc4PlcHzosphen0R1Pj0DaP5cXfo20zh+XF32NkN0fSsZ34iRUlLnX4KeNUdp00fEZHmpUXcE4EM56yVrXrSZto2Mp5ZJ5vyHXOVt9/+/MqgHhJrt04syJbl1LPNLu3VB0+ERUBfA7ABwGcBvAoEZ1OeN8igJ8E8DXXzzSBbX179sEredlMD3ZkJW2rlg8yz8o1zKOixomRpYTgLoeP0mwCbo406VQooAiEfU4oleEz1oyr340mAiND+UU5tNnGcWqc8c81tZs1n2wP3c12H43btDphn7IxcUiEnNGdLTgY/vsAXBRCvCyEGAJ4HMAjCe/7PwH8LIA+w2dqw9bhK6kkTccFzCe+OgCW1gYBMJ/4eYk7+R6zia/C+szvbskekxa9smuzQOci6ST0Lg9tliz19py8gFtOiC8Sy4ts4+8xQVr5qNMmMhpve/gJ4F4zn3rvHSTCpLwAIMd6q7VWOAHgUuzny8HvQhDRewHcJYT4gyxDRPRxIjpLRGdXV1cZhmbPdNSinj0VqmwCFvJLVghumbjMlh/s6vDTnhXqMk4gZzE5SG9JLA9wk/NSowbr/E3yqdDQpgPDT0uwAvYSYdZmbzqfsspHnQ5JpbVrCBvn8Uk6gL38kpYXAODUrsIGc0/aElEBwC8A+Jm89wohHhNCnBFCnDl27BjL59tOUrXoZ2t8gfk4Z+ekLesmoqKG7dNDbYC2LDfJkcjPsj/MlinpWI0zPRJx1fBn203HbVrnhDL0dtMEa578AJhLJVk2XRKs/ZSWIpViwbpLbi9FJgLcCETWJnKrJW2vALgr9vPJ4HcKiwAeAPCfiOgVAO8H8MROJW5tH1qRdlAEsJ+kedl6wGJjChlZlpbJF4m4njbNlnT4cg2hzTnkBWw3u6R208omYJET0qn6Mvz+oYbPKJVkFha4Vn0ldLN16ZKbGd1Zyi/9UXKZq7J5q2n4XwdwLxHdQ0QVAB8B8IR6UQjRFkIcFUKcEkKcAvA0gA8LIc4yfHYuFKMaGOrYaWWJQHyB2unt1ZSHlQD2MhFn0jbLJmDHdNIeDq3gIumkjdPWZpreDMD6ATDycFwKcyzb9e7POi8QtqswlDWizT79rIi1TWaJsDtM1vABtxPR6ffevgCEe97bwtnhCyHGAD4B4CkAzwP4ohDiWSL6LBF92NW+K6xljdEk0TEDcYZvHtpSwrNC4+PkTNpWSwUQ2UcN2VKJXVif5fRs69tTyzId2HjWdx9PzXNCWZGIbWfPvLJMK5tz0PAzE8GWz4ieTkWqpAOop3NZaPgpbSUAF4kw3abtHLVF8vZoCCHEkwCenPndZ1Le+wMcn6kLa208J6wH7BZTal7AVn7JCJej0NZyE0mVtMz7tGSxPPX7NzeHRjaBHPnFkj11hxOsNCqJr8WjpqSqi9RxjibpbDRkznbyS3aFFn/Vl+l86mbMUdtnRGcVFqjfW0d3GTbbvZGxzbwiAJumgbY4ECdtAUsNP/Um2R3dzrIZVtRY1vZnas6G0lOuTQtHmsUcbW2Gp0KZ9dFsSUclQ82vaRZzVJ9rajPrvABgHoVmavhhtGxX8cY6n8I5mhKFO5yXSNPb5yIR3oJJ2z0N2yPRWTcpTLAaM+f0ENQ2tI16/mTVDtslAzlD2/yowdzmcDLFVGR/9/7IvCFdZqWGJYHoZ5WP2tqcRxSqIRFy2gSkrGEeNUi5Ji1qkjbNJJ2sthK24wSCQ2cZc9S2aaIN9r/DL9mypymqCQekgBgjs0japmnYtqFtLyMvADjKL6ks1/zhGlqRiLHDywnrLRvSZckvTnJeRiJYvcfIZmbEaL8xAdsfKAPEJULbRHA6czaVNaJx8kUNg/EUQvDaBHLyApZz1Bb73uHbJm11GL7VAk2xCdhNqP5IPsA8KS/gYhPIcs4Wm4hOIph7Y3K4T1mVP7Y207+7bdI24+SyrfQ0lA8VqaTkJ2zuU1auQf3efJwaGr5tFMqctO2nlI/GP2unZJ397/AtK2qywnqXU4xpkwmwY7lZzFHZtE4Ez8E557Enk8NHOlFD/H06yCsfdWH4eSzP/D5ltYCwyzMp+SGNQNhIhHlyntNmnyPncdu0kV/ykrbxz543DozDN5YLxuk3qVggVErm9dhpDzBXsHkQRtZkimyaM0cgS9LhZ0+1chFCmB0+yss12ORvsurQAbcE6zz09rR7VCoWUCmaz9Gs6Aawq0XPJRAuSdvUUlfzp4hlVRPFf28iv+TlBTzDZ4aqpedkZIBtGKrh8K3kh/TbaBU1BO9PzwvwMzKbFsm5i94iutNxTqY2gZwDPQ4luZkRo+XGnLbZAfORdKzmfcZTyYD5VJKF7aENxpqbF7Dc7G2x/x2+xeGj6ClSOVKJVaVGlk1zbTztKVKhTQtGpsaZmhewkp5yogaLiZ+ruVqEy1mnV23HCeSdtrQt882fo6b3qTsco1FOP55ju4lUSwUUEspHbceZmwi2qNDSiUIBs41Ze456hs8Dm8NHWQ3JFKQj5ZVfrJxzhvQEyColzooSQE5e0w6kOu0a1GebjFONJwnRc231Q3s1zqwj+/H36SCPQFSKBRQsTkRnFRYAwaEe44gx+aEioc2yeUVNXrFCw4qNZydtGxbyi24RgFUUylxNZYt97/ABczae50gAGTlwO1Jb55ynuXJWlCib6n260GZPu6y3RzpuesdEwKz6JY9AEBEalZJVLXrSg0oUbKS3tIeKKNhKmbl5JsYGf4CdTKZTrBD/bCObjJuICw6Ewzed+Oq0a6Y2bsXGp6n9eQC7k3x5Yb1ttULWd7c5eKbK/crF9LAesGNPuVGDxQJNPyBn/owBHQJRs2HOuZGY3RmM3LyAqUw0h3LkXA3fgUCwRqE5Nm3yAi44EA7f1DnnLXogCG0NblJeuZ+yaX56N7/UcziZYmwgv+TmBSwXU2ZewGLi69RNA2YJ1jybNtUveWxUvlZgPbmtPs8qakiRswCXqIFfIswkEBbOObd8dA6RrS/LnAOalSI6A15G1jBcTHk3Xr1mJ+lkRSLmCUFdScfEOeeW+5UtbGqGy9zOuVY2c/jzuPfjyRTDST6BMC8syN5EGhULSUeDlMjPNrn309zCAvXZ+jZ1CYTZOIHs5LKpTRccCIdvynQ6Qbi4UM2qVjBrL6BsNjJsNiySbHmMrG7RererkRcwtdnP3ZjsJZ3UFhhqYxroJ23zSgiV3a4RgchOMKrXbGTHtEZfgH35bF5ZZndkdkAu7QHm4ThtpDeN8wKAqfySfU2dCETOJuI1fEY0K6XQ4eogjzlKm0V0TBxJ4CCamdUKJQzG+vKLEALd4QTNavY445+vNdbhOHOzawabiInNjqZNk0hMnQpN6hYJAJVSAeUiGWnjqqKnmTNWE5udjOfZKphuImqcWZtow9Cmspu32U2mAkMTiVCT4Zsx53HmZhdu9lYEIjthbxrZAunycLlI1o9jtMGBcPi2DD9r0TcqJaMb38np7ic/z2ySDidTjKci06Z6zWTD6wzSm4dJm0Vjm91hettZIL6YTMY5ztzsgOA+WTD8Ztb3rxYNbepuIhYEIneO6tucBA8VyR6nuXPuDMdaNk0dadYmou6f6SZSKxdSZaKGxXfv55BHIkKjbCY5u+BAOHxThq8WU5aDalaL6AzH2qGtmiRZNhuGzFknagg3EYNJ2h1mO1KbiS8dfv4mYqrhZ9kEgkjMZJyDMYjymLPZfOpo3aeSccSkxpJuU0qEunM03JgyNztFIEyuafZmr2yabE7d4QQNjTlqEoV3hpPMKNSKPKn7lJUXqe5cT/wD4fBND6Dk9dpWNk16v6gFouNIdSe+bl5A2jRxJtmOtFm1iRqyQ/BysYBKqWC8mLJsAvLamLHRCRoZyUBAOm6r+ZTFcqtFbNpEIpnzqYSxgfwSPvwkUyJUpMTsPmXLeco561/TzUGOzXATMdvss+Z9sUColQuG9z4/EjMlpC44EA7flOl0NNh4pDnr3Si1QDhZrlnUoDfO0WSK4Xiak2uwyQtMMic9EDhSQ5t5Dl8yfDP5JcsxA9JxGzFHzfyN0aasMZ9M8zfKZp6cBegz/PFkiv5oqiU7GjH8vKjBQnbcHOjMUdN7P0a5KJstpqFRNS/WsMWBcPiNSsko0dQdjlEMHkiSbtPMOetowyFz1pxQHR2bFbMQPGJ5vKFtV4eNG0sl2dowYJ4M7QwmmY4ZcGD4Oc7ZRCLUYviGkVjXgOjoEgiVj8oa50I47w3zAhnXsxo8+tE0EZ53702dcycnagCCeW+wibjggDh8M6bT1QjrI1aiazO/UsN4EwlZXrY+GP983XFmTXyb0Lajw56q82D4hvmbnDJXwHyBdoYTDZZXghDQPhWtw8abhnM0tKkhEWoTiDAfphM18Gn4sl2FWXTXGU5yoztjhp+TFwDkhuclHUaEjlSz+qU7yJ5MgPkk1dPwDReopj4I6LMnHYYPmFWATKeyJzg7w89heYD8HuYbU84mYpoM1WB5TUMJQoeNh3NUl43rMHzDBGtU8aYjj5pp+DryixGBGOQz/Kahc9aLbM3LZ21xQBy+WRiq40hMa9HD6o+UQ0JynKaLPp/hyzKzqPdI/jjz9Wb1mbrfPepAmW/TqPJHY2M2Pi8xmoSH1dJgmgztDPNlItMKLZ3SYWOGr2XTLMGqw/DVHNXdRKI8U95mb5q/yY9CG4an9jsDzajhVmL4RPQwEb1ARBeJ6JMJr/80ET1HROeI6CtEdDfH5+rCJhmaJb1stanPnhrlYmpPcCDGnnQ3Jo1a7LDO13DR531/k0mqU0KoXjcp99Ni+IbJUC2WZywR5ieCm4YRY3cwQSHj4fVbbGoXFuhEDZYMP8MmEQVSiWEUqjFHTe79pta9Nzvb0BmMsaChFtwyDJ+IigA+B+CDAE4DeJSITs+87RkAZ4QQDwH4EoD/1/VzTWCaZNRzJGabSGeowxwN9VFN5yxlDVMNP5896eca8uUs9bruONUhoVypxPC8RN55ASDm9DQlQp3qD9NKFTVHs8tHzed9/O+SoA476Tvn/JJUwOyRhDoH2ZRNo+hOq0LLkOFrnRUpGc1RF3Aw/PcBuCiEeFkIMQTwOIBH4m8QQnxVCNENfnwawEmGz9WG6UGhvIQQYF6LnneYCZBsrUBm4wSyD3UAZs3jOrrO2SB5ZcTw2WWikuF5ifz7ZFypoqkNA2ZSiXaeyTh/k52wl51iDaNQLW3cdI7mbfb6DH84nmI0EewMX6fyp1ktYWqQsHcBh8M/AeBS7OfLwe/S8DEAf5j0AhF9nIjOEtHZ1dVVhqFJROGygcPXlHR0nXNeuwIgFtoaMLJqqYBSMfs2miRYdUoIAbPDbDolqfJ1A5anKpQ0ogbATNbIZ/hm80mL5RmO0yjPpO2cxygVCJWc+dQ00MZ1GX7ToF1FVKGUv0bN82H5m4iZhq+RXLaoUrLFjiZtiejvATgD4OeSXhdCPCaEOCOEOHPs2DG2zw07Rho4Ex02Cuizp94of6cHzPS8roZUAJh14YwYmY5zNiv3y2ekkpHpPIdU5wwCYFb5NBzLlsM6LA8wYPgGUYMRKcmxaS6/yPmUJRMBqj+R2Xxa0Mrf8EaMJlU66pRzXgllsyKfLzHUjRhN5Lwd0PE5HP4VAHfFfj4Z/G4LiOgDAD4N4MNCiAHD52rDtLJAp6pCHcwyCW3ztHbAPBmaF4kAwcnQXcwL6JwIBmKNuTS08Y7GGYT46zrXNBynhjYsbfJFd8ZtNTRISaFARtp4R0N6AuyYc34RgL7sqJsTMqnS0ZGz5OuKPOaPdaJZjmxakusCDof/dQD3EtE9RFQB8BEAT8TfQETvAfArkM7+DYbPNIJp//aeRoIVMKvJ7WqE4IBZZ8/uYKJlU7Ys0D8vUAn62uTZNNlA5d/wJcJ1epTEbeqMNWKO3BVac9DwNUgJYFb51NFIWgJm2rjufGoYrCWd8lEgqtLRSYbqHGSTr+s7Z/0CCPPWErZwdvhCiDGATwB4CsDzAL4ohHiWiD5LRB8O3vZzABYA/A4RfZOInkgxNxdUigWUCqSlj+qG9YC5VJLHHgBzhq8TNZiUJnYHY61xNiryATA68ovOeQFlM/7+LOg6Z5ODQtrlfgbOeToVgfySvehVwt6oSkfLOZto47qbiH71S0dzPpn0UdKVHRtV2btfJ2Gve+9NnLMuKVHXfHMHJJ38GaMBIcSTAJ6c+d1nYv/+AMfn2IKItJOMumE9YH74SIeNN6pF3OwM9WwO80+FAmYlb1LO0vvuQPC9cq6VflWFPhvX6QcfH6eWTV1GZsDwVVSZ50iNa9E1ozsThq/TVkLaLGJ1Q0+V7RiN0zQRrJlrGWY/HUuOUzdq0HfOkU1NUrID/XQOxElbQL+cSj2EQksbN2HjOe2BFczKHfO1YUBpmSaLXi8vIMegF9rmHRIC5sTwTWyqg0caD1WJv19rnDoEwuAcghFzNsgz6RAIkyhUdz6ZtKsI71OOEzfpia8tExk453CcmtVUJocObXFgHL6u/BLdJL1JqqO3jydTDMb5h4QAQw1fQxsG5ISSdcb5oa3OUXBpU/+0qWJ5edUfJuWzXU3NNWoex8fwo+ZxGpudZh26+lyd7y6E0CrLBAJt3OREsCaB0G8BoTmfqiUD+UU+mSqvHNmkJ772eQED56zTRwiIz1HP8NmgezJUd9EDQL2st0B1WsQqmDyhSdc5myVDdSs1zBi+bq4B0GNP6prqtIBQY8iDToO7uF2d765bQggoR5pvczCeYiryIxE5TkM5j5vha1b+mPT9kS0Q9Oe9SYJV56Rt/P06NnXPS+zEYw4PjsPXlXQ0y7MA/VYAUVmiHiPjrP4AzBKXOiWE0qZZRY1WgtGAPXUHk9xnFgBmteg6D6lR0GW5YR265vfXeeqVbkUJYHZ6Wef8ibLZH00x0TwvoZsXAPTkF50zCEBcfjGI7HNPres7582BHoGolWXCfid64h8gh2/G8HUnvl64qJ8XaFaKGE1E7sEOVf2hUz5qzPA1E8EmNnW+e1g+q8mcG5XsZxYAshZdtxWAzpPOFIwZvqaDMilJ1dfG88c5nQp0R3pVOk1DlqsVNRjkhDpzYPgqx5bV3BAwZPiaiWDTE/YuODAOX7dznq6WB6gqHb5yPyB+Kjh7rP2xyTgNmI42IzNLXmmdFzDS8PVsKrs6NntGm70mgdAsIVQ2TTYRvbJMPYbfH08ghG51mpk2rnsaXL0/D6YRo+5mrxXZBu/RisSMIvud6Zh5YBx+XdM5m5RlNitFdDVq0XVLvpRNIJ+VRBUlfDYBc81VyzkP9SpKaqWi7IuuWVWhYxNQrQD0Fmjek6kUmprPtdWtJgL0WwEYFRZotgIwITomfX/mITtuala8mTSP041ETJ72ptvzR77HM3xWKOech3CB5mh5AKLH0o2z7XY1Ozsqm0D+xNd5FKGpTRXW65YQ6tgE9Gv7CwX93v1dTZuAYs56Gr4OG1M29Ri+/mav2wpAtz0wELHLvIjRVMqUf5NtMzx0ZsCctZ2zgU3d3JV2xKhZOq3b3BDQLypxxYFx+HVN9mSUtNXUsXWe+qPQ0Ewy6tb4yvfo2VRhvRHD10yG6mx2gH7SWvdcAyAdo06pq04PpdCmtoZvlhfQqUU3LR2W49CLGHUryeTfZNvsmVSnmRAIzVPrRgl7TYYPGFT8acpZgEque4bPBhXa5tWid4d6LWKBSG/P20hMwnrduvGI5eknWPOcnolMZPLoxI5Gu2kF3R49ujouoK+N6zwAI7Spqbl2h2OtaiJlU6cW3aR0WPcwmxHD16xvNypJNZQIdaqeTJrH6TSjU9Bm+CakxKAhnQsOjMOva7Jx1dUyr/oD0NfGlaPlTDSZJIR0n3xkIhOZPDqxp3n4BggiMYMqHR2YaOPsDH8w0aomUjYBHUdqVjqsxqFj04jhc0YNiuhoFgHoS296Z2V0zyBIm/rJdZ2NCQgqtHzSlg/K2epomdrasDHTMQnBs2325hA1mMhE0m6+czZpRgeog0J64bK+TKTP8HUOiAH6tehG80lTKtE9ZazGCWhs9kZnEDQjWwObKmGf992N55PmYTaT/I1u5ZPOg5RCmz5pywvdmlzdQx1xm7nhssYDpxUimUiTPWlMUt1uobpdLRV05BeTQ2cAtHv367YBAPRLck0Swbqac8dgPunWoqvrU9coLIge1qLJ8LUiRv21pGtTN2FvEi0DZgxfm41rRqGbGk+7UvBlmczQr1YwYI6ayVCdB04raCeCDQ706HYL7RqE9YDe6WXdfiIKOr37hRCBhm8Qgmuf4NR3JOpvsqB7SAiIOfyc+aSS4HmHhIBYaaKuhq/ZGlv+jWZkq50MndN8yrEphDDS23UfZG4She7Ug8wPkMPXC5dNkzeAfHxhFnpGUYOZhm9SSsaZuJPvy5/45jbz2fhwMsV4KoxsDsZTjHMS9rpPfAL0a9G7mh0ogfhmrxE1GM5RXTlPK2IsFVAuakSMBjYB2X4ivzrNcD5p2FTzSZeN6zJ83WcWyHEWd+RB5gfO4efV4vc0j5cD+gc7dOvQgdhiyi31HINIVsvojjXPpsmilzZLudfTpIQQUKdidR2JmXPOG6tuzThgxnL1NXxNhm9YQiht5m/2RvNJY2M2ZeM6FTUmyWVAj+F3DeeoNsM3WPcLmnKeKw6Qw9dPNJkv+vyEmG4yEJDabK6GP9RrOazQ1DhtahLWS5v544zaShgwfN0yV0P5JUvOC1sOG7LxfEeqLxNp5wVMqlTK+gl7s/mUL5OZJJfV+3TkLBObOs3jTFpVqM/WOb28qfnMAjVOQK/9iQsOkMM3SNpqOue67mIyYHmAXhMtk4oSQJal5jJ8Q5lIh+WZnBeQNvPPS5gkA+OfneWg+qOp7CXDXaFlIBPp1qLrtisAgFKxgGqpoCll6s+nhsYcNSkfVe/TZ/j69z5fyjSdT/kEYhRsCAvam51++xMXHDiHr5O01Z1MxbATY35lge6kB/SO7es+xFqhWcl/sIqpTKQjv5icF5Dvy99EQx3XOC+SYdOiQin+d2kwkYl0a9F1HyoS2c0v+TOpGQf0DgqZHGKUNvPbQ5t0npXvy6/S2TScTzr3Pjqxb0ogvMNngW7Jmylz1qkAMdHwAc0FahDWA6rckVcmkg3pNENww9LErIlvzMg05JeofFS/ogTIDsFNZaKwFl1jYzba7DVK/kzKkQFN6c3g0Jm0mU90zOWXYu7T3kyTyzrO2aRxWvx9836Q+YFx+NWSagWQfkHlxBBGi0mnr4adhs+XuAOU3p5v0ySsV1pm1mKyZfhZuqsxy9OQX2wcSfzvkmAqE4W16Dp5ARMCoXGoxyR3BWgm1w2qVKTN/JYFug+vVzC796b5m3SbJg3ugLiG7xk+C8KHDGTcJNNDQoBeiZZJUy4g0PBzSj1NSvMAFdrmRw0mC1RHfukaOmed1hI9g+6j8fdlfX/TaiITmchkY9atRTeyOQeJUK9KRz8fBii9Pbt5nLqmOofOAL1SV5PeREC8mip/Punep6hK5xZg+ET0MBG9QEQXieiTCa9Xiei3g9e/RkSnOD7XFPVKMbNm3mqBaiwmk14yoU0NqcRmnFmLyZjh64S2wwkqxQLKmjquTqlrx5TlqUXPyMh0atFNuqQq6NSidw3lPB3m3DFoHCdtakiZBidNAXmdxlOBYVbEOBijXi6iqHHoDIgYvs580pW0dJyz+XkBvQotVzg7fCIqAvgcgA8COA3gUSI6PfO2jwFYE0K8HcA/BfCzrp9rg7xWAOpi6zw2UCGvralp7w85znw23h1OUC+bLfpJ7mIyyzXoyC+9oX5pGqDH8M1bQOhormYMX743m+VuGuq46vOzxmkzn7QYvkHjOGlTo0LL4KQpEGPjWc7Z4IT1Fptaejufc1abgUm7BjmWvc/w3wfgohDiZSHEEMDjAB6Zec8jAH49+PeXAPwg6WZyGJE3SaNkIN9iUjKRySailQy1COuBfJZrmrhTf5cG04R1JL9oMDLDBZpl0zSsl+/NZrnRuQZDvV1HdjS1qSETmUmZxdxWAKblyA2NwoquRdQA5DB8g95EgJ5zNiUlqt34vHviczj8EwAuxX6+HPwu8T1CiDGANoAjs4aI6ONEdJaIzq6urjIMbSvy2JOpIwHya+ZDmchUyxzlaZnmibv4eNJsmjo8IEfDN5SJ1KLP6rPfHY5RKxe0w3rVPE6HkXHWokcsz7QIQEN2NC0syKkmMulNJG0GT3vLaAVgUuIM6LWBMJ73GmxcVT3p9CYC9Bh+GN1pfv+depD5nkraCiEeE0KcEUKcOXbsGLv9xVoJG32N0M4kZKwWsdEf5do00/Cl/JK2mIZjeajDdNHL8WTrjjbOOS95ZcZG88vTTNkoEeX2/VHJZZNqqmalmFk3Hto0kd5yatFto4Ysm4OxbPNsyvCBvES4YRRaVfc+x6ah9ATkRIymObZy/jhNq4nke+ffMZPD4V8BcFfs55PB7xLfQ0QlAEsAbjB8thFWGhWsdYepr6vXVhoVI5vt3ij1QeZr3VHwvrK2zeXgva1e8ljV75dNbNbld2rlfH8Tm4dqcjK3e+kb3kZ/hEWDSb9YK4MIaGeMs90bh5+ti6VGOfO7t3sjFAtkdPhoqVFBK+O7q9eWTK5pvYx2L92RrAeExSRqWKzJ5nGDlGcvKxK0aHBNF4L3ZhGo9f7I6Hqq927mkDLT5LKOTZNNpFQsoF4uZts0rCYCgo35FmD4XwdwLxHdQ0QVAB8B8MTMe54A8NHg3z8C4E/FvPuAJmC5UUGrm7FAg9eMHGmjgqlIn/i2mwgArHWSxxqNU9+m+k5rKd+/P5qgP5oa2TzcrGTaVK+ZXM9igXCoVs602eoOsdLUHycAHG5U8sdZL2sfEgLkJr7W0SEQ+t9/JdiY0pZHy2I+qXuaNveVTbP5pO598ve3mU8r4RxNv6at3siMPCmik0Ke1OctGYwTkOspaz61e5Lo6MqOgNzs1zMIBAecHX6gyX8CwFMAngfwRSHEs0T0WSL6cPC2fw3gCBFdBPDTALaVbu4ElhtlbA7GqU2PbnaHKBmyvLxJGi5QAwcVMvwUm8rJGG0izWyGb7MxHaqVUaD8qOGwoXNeaZRzIzGTcQJqs89wJBabSF7E2O6OUCkVjFjeSqOC8VRgI0UuUCTAikCkjPVmx2ZjUqQkez6Z3Pu8jQmQYzX57vVKEbVyIXNjbnVHOGzw3QH5/TPnfcdmPpXDezEvmMXFKRBCPAngyZnffSb27z6A/4bjs1ywEpNKblusbXu91R1iuVExYnnLMYd/Cs1tr9/smEs60QJNnvihTNQ0Y46ZNi3GWSgQlurpznkyFWj3RkYsD8iPxNY6I9x3+6KRzZVGGS+/uZluszs0+u6AvPcb/THGkylKCecMlE2b+dTqjHCotn08VhFjME/SIsZIdjSI7vLmqMV8Wq5nk6fRZIqN/th4s8+L7m52hrj3tgUjmyvNbFJyszsyd/jNCl58PX2OcmBPJW3njTwGsdYxCxd1bLa6Q2OWF0klOVGDCdMpF1EpFXJtmjrnlYzF1O6NIITZopc28xn+YZtNJMXhAfLe23x3AKk6/lp3ZOyc8tj4WneIYoGM9PZwnJxRaLiJZDN8k2taKhZwqFbKWEvmREeNIZvhm7PxfFIytIoa5s3wD5TD1wlD57FArVleqk1zRkZEmZqzTdQAyLHmyUTGkk4zfTH1RxN0hxNzDb9ZwcZgnNr3x5bhA9mO1CR/AcTYeMa9X66XtUsIgWie3MydT/pjXayWUCpQ5ryPf7YuVprpTs+G6ADy3qeNczCeoDOcsJMSU+lJjbM3mqCf86AeFxwoh5+XuGwZJhgBDanEguVVS0U0KsUMm0NUSwWjEkI51nQ2br1AG5V0qaDjEjWkLXrzxLq0me5IhRBo2YTgGtKbTa4ByIpCzTeRaGPii0KJSDJnRtlRjjX93quNwJRAZCVYo6jB/N63eiNMUqrz7KKG7PvEgQPl8HUSl6aTKS9x2bKIGoBsp7dmwR6AbDYeSTrmCzQ/EjF3zt3hJLGMMIwaGB1pdzjBcDK1j+4yGKmtTJTFnE3naK1cRL1cTB2nZKNmUagca0bEqDb7uqneXk7f7CznaBbDtyU6y40KhEBiVU1/JKMG0/uk5vQ8ZZ2D5fAz2LhieaYLNC9xebMzNGY5gHLO6ezJdNID+cy5USmiWjKNGtLZk8tiUmPaZtMyagjzIgmLyaZKRY4hnZGFUYOhzaW6PIeQHYXaEIis+2QeiQCSQGU50oVqCZWSmYvJ0rHV+M0Zvjwrk8TGb3bsNpGsiDGMGqznvXf4LFCJy6QLGrE8O0ealWiyW6BZztmW4aeP02XRp+mOYfmotVSy/fu75Brifx+HzbkGOYb0cW4MxhhPhfE1VecQ0hb9zY55wlqNlT8KTSc6NvIokB0xRhuzedQgRPIBwZblJpIl50XSk3kkAqTnWjhwoBy+SlwmOT3byQSks3EhBFq9kdUCzWb4dlHD4WYZrd4o8VCPTYJRjVP+fQIb745QKRaMTjECMfaUkBuwlXSyNxE7bbhZKaJcpORNpGN+ylZBShAZUajFvc+UCK3nU3ZOyPR6AvLed4aTxLMyre4Q9XIRNYNcAxBtzEmRg+26z5KH7SvesvOBHDhQDh9In/i2ycAsm+v9MSZTMRf5xTZqmExFeDw/DtO2CnGb6u+3j1PaNNWGs0Jbl0Rw2jhtTsQCUeIycZyWchaQnmuxzTUom9wykfruSQRCJpftIhE5piTnPLLcRHScM1+xxk1LAhGeXvYaPh/SmHO4QC0m1FKaTYeoYaVRTtQdVdRgIz1lOVL7RZ9ej21TmgZEck1SaLvWHaFZKRprw+rEJWdeAEiXNWw3Efk3yZt9KBUwSoTTqbAqSZU2yxhNRGITsTWL/IUaJ5B2791ISTLDl/PJNHelQ0pM536lVMBCtZRZ7umKA+fw0yb+PBZotInYOeekKgAVNdhuInJcyRueywJN08ZtvnvEyJJsmpe7xe0mLXo1dnXS0wRppYm2eQH5N+VEOcstCk0mEBv9MabClpRkJNcd8gJAupxnxfCb6bKj7Xw6VJN9cpI3Zvv7tJzTn8kVB87hp4W2Lgs0rYzQyWbKARxbfTD+N7M2p0ELBJdFnzjxLRd9rZze/8TWJpCeEGx1hzhUKyW2R8jDSor8Ml8CYXfvkwiEi/SUxpxtWyAA2ZKOtUyUETXYziciwnI92ZesdYdYrJW0H+sZR1ZehAMH0OEn645hja8lywO2MwjbBGPc5uzNt61tj//N7GJa748wFfZsNMmm+p2NTSD9kJhtSSogk9ZpNl2ihiwCsWQxn9IIhItzTmvX4RKFppMSu0oqIDtiXLNocgbIh9qktRVxufdpbNw2EpE2s5uyueLAOfyVRhnj6XbdsdUdObC85MXkUvmTlmhyYXlpbZddNpHwUM/MAhVCyAVqseiBbDbuspjSFqjtxpRGIFyihlQCYXleQNpMlvPcItvkcbpFocmbyHgylVGoxb3Paiuy1rGTMoHsXIttFHq4UfZlmZxIW0w3LdqZKqTpjq3uCAUye7DErM3ZcNm2nwgg+21TwqlgF+Yo/2574tIl15BmE3BbTFkJVvtFLxOXs09Uco0a1LhmbQK2UUNyBYhrIjhuY3acNjbTTgWrBnX29z4tYnSYTyk9n5wZfkaTP1ccOIefvpjcWB6Q7EiXGxWjRlfbbc5uTPZsvBieCk5jZC5sPNmmywKdtTkOtGHbcaY9nWytY3dWQtmUNvjmUxqBWOsOsVQvs0ahLpv9oXpyWxHb06sKSTq2TUfPOFYSojuXXIO0mUIgOnb5MCBq8pf2zA5XHECHnx7aWrM8VQXQ47OZVgXQ6g5RICT2Stcaa6OyLWS0PQqukNSrJJSzrCWd7YtJXV9b9rTSkE8nW+9vd6T2m31yBYjLvU8nEA42UypVXKJQRSC2zyc355x0712IDpA8R6NTti6SzvaDjDedZKLomR3zwIFz+Fls3IWNKhtxuMgPoe6YkAheMmyPu3Ws26tKbNotx5F0tsFFG1ZjmWXjLvXyQKy+P8b0BuOg3bL1Zp/OnO2lgjRSYr8xpbUzdolCgWSpxCUnFNnklR2TqvNsevZvtVnBcDxFL9ZWpD+aoDcyb9+tEM6nOck6B87hR+Hy9t3eNgStlYuolrYf6nFhjkBy4tK2541CUjtjFTXYsLzQZsoCtZVKlhvlbWzcRRuWNrdXgIQbk2v+JoE9ukhPSTZvduy14bR2xi7jBJSOvZ3hVw3bLceRRCDUerX9/oeDcSYRCPuIcfvGbNumI7KZXubMgQPn8Jfq22/ScDzF5sBeywOSNUKXsF7aTJA1LE8bKiRvIu5Rw+yhHpcKpfjfxe+TszacEN25bkxJlSqu80klLrc7UkfnnBDd2TZji9u8OUMgVGRr2lJDIVEidGb42+U823bLcZvAVvLINu/ndPjqwDl89Ri1eOc8pZe5OOe0kNE2tJM2tycuXRJCgNpEtofgLjaTDvW4aMNAco03R+IO2Crp2LZGVlhKeA4rx3xKcqRrXVfnvP2ksWsUupIShbqSklkC0eqOUCubP/RH4XCCTOYqZUbnWmI2HXMNaXIeFw6cwwewrY+37VNvtticmfi94QSDsV2jq8hmGsN3sJnQztg1akhyzkrDto0aknItzpJOQuLSNdegCESckbnaVH8b/+62j3bcajM51+K0MSU8ktDlrAQg5/1sO2OXfBgQ3Yvkzd5Nb48nrW0bp4U2vaTDj9kj0a43HsC2p9hHIagLy9teBeBSqQEkV5W4Rg1J2rhtoyuFpENia127RzsqJCUuXU6ahmOdKSN0aZoX2SwnkhLXaxq3KQ/HuUahZQzGU/Ri5xBc2l8AyaeCbZ/yFtpMIBCt7tApakg6HOkaheY9ncwVTg6fiA4T0R8T0UvB/1cS3vNuIvoLInqWiM4R0X/r8pkcmGVPrnXokc3ti96V5cWrAFwrAIBkBuEcNSSxcQbpCeBd9FHiMiG6c7xPawmRCOt8csw1ADLCacUIRG/kHoUeTpxP7pIOsFXHdjnMBCTLebJVg9tmB2wlJWGeyeJwnMI8++m4MvxPAviKEOJeAF8Jfp5FF8CPCSHuB/AwgH9GRMuOn+uEI80K3twYhD+/uekWhgFBQiymO65uSvtHFtydnppErkmm+N+uzUx8V71Z2YlsujHH6FnBfFEDILVcdb8BeW1tHqoRx6z05srykmxyEIiVRgXDyRTdgI27lk/Gx6Pm6HQqYPsErWicyXo7h+w4S8pcrme5WMDiTDvjtY794TiFpHMIXHB1+I8A+PXg378O4L+efYMQ4kUhxEvBv68CeAPAMcfPdcK9ty/iaruPdnDzv319HQvVEm5frFnbPHWkiclU4JUbHWnz2gYA4L7bFq1tvuVwAwDw8qq0+dLrmwCAe440rW3eHfztd1alrdfX++iNJjixUre2edtiDQUCXrvZBSBPxL56o4uTDjYLBcLxpXp4PQHg4hubuPtIw9omALzt2AJeen0j/Pnl1U3cddh+nIC8T6+82Q1L/q6v90EkiYUt7lyuY607CufolVYPAHDboaq1zTsOyfl9aU3ep2uBzWOL9jZPLMtrp+79lVYPUwHcsWS/lo4uyPFcX+8DkPPpWrsXjt8GC9US6uVieB2V/aMO3x0ADi9U8OZmRB6vtHq43eEeAdkPXXeFq8O/XQhxLfj3dQC3Z72ZiN4HoALgOymvf5yIzhLR2dXVVcehpeOBE4cAABeutuX/r6zj/jsPWScYpc2lwJa0+ezVNk4s160ecadwf2DzfGBTjff+O5esbd65VMPhZiW0ee6y/P9DJ+1t1itFvP22BZy/3AIAfGe1g95o4mQTAB48sRRez/X+CK/c6OLBE242HzixhFdudLHel9LG+Svr4b2ztnnnEjYH43BzOn+5jbcdW3CKGh4I7vGz4Rxto1kp4pTDZq++5/ngnqs54DKf7rtjAeUiRXM0+L/LfTq+VMNSvYzngu/+ndUO+qMp7g/WrQ2ICPffeSgc32A8wYuvb+D0cXubAHDvbYt47tp6+PP5K22n6wkAP/WBe/HpD73TyUYach0+Ef0JEV1I+O+R+PuEFAa3P+sssnMcwG8A+PtCiMRGEUKIx4QQZ4QQZ44dm18QoBbThSttjCdTPH9t3dmRvP22BVRKhXBCPXdVbiIuWKqX8ZbDjWgTubKOtxxuOG0iRIQHTiyFjv785RYKBJw+7vb9Hzq5jHOX2xBC4Fzg+B86uexk88GTkXN+7qpcVK7OWd2T566u4/X1Ad7cHLBsIoBc7EIInLvSdt7sHpjZ7JUjKTqQkrcebaJZKW7Z7G9brDqx8WqpiO+5YzHcRM5daaNcJLzjuH1kS0R4MDZHOTYRQF7TZ6+uYzIV+Pa1DYwmwvk+vevkEl5e7WC9P8IbG328vj5wXvf/1d2HcebUYScbach1+EKIDwghHkj478sAXg8cuXLobyTZIKJDAP4AwKeFEE9zfgEbrDQrOLFcx/krbbz0xiYG4ykedLzx5WIB77xjEReurKMzGOO7NzrOOz0gJ3l80T/gwHIUHjqxhJfe2ER/NMG5K23cd/uidaWCwrtOLuFGZ4jLaz2cu9zGQrXkJD0BW6OmCwxsNP73F660w+vquujvvT3a7K+v97G6McBDjs7pcDBHL1xdx3gyxXPX3CORQmHrZn/ucsv5uwPRHBVC4PzlNr7njkXjRwZus3lyCS9c30B/NMH5K200KkXcc3TByeZDJ5fQG03wndVNnGO698pvXLjSxrNXJClx3ZjmCVdJ5wkAHw3+/VEAX559AxFVAPx7AP9WCPElx89jwwMnDuHZq+ssYa3C/SeWcOFqG89fW4cQcN7pAen0Lq/18NqNLl672WUZ5wMnljCZCjx/bR3nL7dZJqhi8+cut3Eu2JhcJDIgWjjK4d9xqOakNwNSr77jUC289xzRTXyz/9alwJHctexkE0AoQVxc3UR/NGVxzg+dXMJz19ax1hni5Tc7zlEYADx4Yhnt3giv3ezi/BWm+XRiCeOpwAvXN/Ds1TZOHz/kFN0AkXM/d7mNc5da4abqZnMZgJTJQl+yjx3+/wPgh4joJQAfCH4GEZ0hol8N3vOjAP46gB8nom8G/73b8XOd8eCJJXz3zQ6efvkGmpUi3nrUjY0qmxv9Mf7ownUAcNIc4zYB4LfPvrblZyebwcR/6tnXcaMzZHEk7zi+iHKR8I1X1/D8tXW8i8GRHA4jsXVcuLrOEt0AcrO/cKWN85dbePttC87RjbQpN/tzl1soFchZGwaiOfoX37kRfoazzZPLGI6n+N1nrkAIOEe2QDQn/+D8NbR7Izx4YtnZpvqu37zUwrNX3aMbALjn6AIalWIY3T14Ysm6/YPC4WYFJ1fqOBc4/LcebWKhane6fCfgNDIhxA0AP5jw+7MA/ofg378J4DddPmceULvwk+ev4aETy85sFIhyA7/7zBUcblacqgoUVJTwO2cvb/nZBSpx+6VvXAIgnYArqqUi3nn8EL78zSsYMkhkCg+cOISzr9zE9fU+fvih4yw2779zCX/67TewujnA33jHbSw2HzixhM9/7TX8wflruO/2RaeEbdwmAPz21y+xkRIlNX3+a69u+dkF992xgEqxgMf/Us4nDgJxcqWOlUYZT3zrKrrDCYvDLxYID9y5hK999yZefH0DP3Q6s8ZEG+86uYxzV1oYTwT+2py0dy4cyJO2QOScXbP/cdx3xwJKBcLNzhCnjx9yZg+AzDecXKnjjY0B7lyq4ciCm6QBRInbNzeHKBUI77jDPsEWx0OBjg8ADzGwPECyx2vtPoSI7pkrHjixhKmQNdlcequy8+qNLt51F984AeDb1zdw/4klFlJy95EGFmslvLzawYnlOst8Uonb1252USkWcN/t7vNJzdFvvLoGAGzR3YMnl/D8tXVMhXtRQdzmpZs9XGv397R+Dxxgh6+0XIAvyVItFcPJzsHEFdT4OFiOgmJ2XGwUiBbQcqPsXNuuEP/OXN8/7jw42Cggr2O5KB0yh6QByDmqaro5mDggHan6zlwbExBJQ+84vohKicetqHFWSwW8/ZhbwlYhvta57n3cDhd5nBcOrMMHooXPuSsrm6cZHf4Dc3D4yhbXpAeAdweJSg5tVEHdm6MLVecDLQp3HKrhSLPCkrBVqJQK+J4gUuK8pur7c0lk0uYyAD6GK20ubfk/j81lAMA7jx9yOrm6xWZwHW9brOJ2BskVmA8pmRcOtMP/6/cdw4nlOt7KxB4A4N13yXZCnIvpPYEjfTdD5Udo8y3LKBcJ77uHT3N827EFHF+q4fvffpTN5pGFKk6u1PHQSb5NhIjwnres4J3HD7EkbBXedXIZ9XIxdPwcmMdm/+45zKd52FTOmXMTuedIE4vVEuv6PFQr463Hmjh1pGH96NGdAs0+j3Gv4MyZM+Ls2bO7PQxjDMdTnLvcYj04IYTA2VfXcObuFTanBwDX233ctlhl0YYV+qMJKsUCq83nrq5jsVbCXYfd2irEcWNzgNFEOB06msWbmwNcbfVYncmbmwN85fnX8aNn7mK799OpwH/5zpv4/rcfZZ1PT798A2fuXmFj40II/Is/vYgfuv92vOMOvoj5P73wBu5crrPkGhT+8Pw1TITADz90J5tNWxDRN4QQZxJf8w7fw8PDY/8gy+EfaEnHw8PD4yDBO3wPDw+PAwLv8D08PDwOCLzD9/Dw8Dgg8A7fw8PD44DAO3wPDw+PAwLv8D08PDwOCLzD9/Dw8Dgg2LMHr4hoFcCrDiaOAniTaTjzhB8nL26VcQK3zlj9OPkxz7HeLYRIfEbsnnX4riCis2mnzfYS/Dh5cauME7h1xurHyY/dGquXdDw8PDwOCLzD9/Dw8Dgg2M8O/7HdHoAm/Dh5cauME7h1xurHyY9dGeu+1fA9PDw8PLZiPzN8Dw8PD48YvMP38PDwOCDYdw6fiB4moheI6CIRfXK3x6NARHcR0VeJ6DkiepaIfjL4/WEi+mMiein4/8pujxUAiKhIRM8Q0e8HP99DRF8LrutvE1Flt8cIAES0TERfIqJvE9HzRPS9e/GaEtH/FNz3C0T0BSKq7ZVrSkT/hojeIKILsd8lXkOS+MVgzOeI6L27PM6fC+79OSL690S0HHvtU8E4XyCiv7Wb44y99jNEJIjoaPDzjl7PfeXwiagI4HMAPgjgNIBHiej07o4qxBjAzwghTgN4P4B/GIztkwC+IoS4F8BXgp/3An4SwPOxn38WwD8VQrwdwBqAj+3KqLbjnwP4IyHEOwC8C3LMe+qaEtEJAP8IwBkhxAMAigA+gr1zTX8NwMMzv0u7hh8EcG/w38cB/NIOjRFIHucfA3hACPEQgBcBfAoAgrX1EQD3B3/zLwP/sFvjBBHdBeBvAngt9uudvZ5CiH3zH4DvBfBU7OdPAfjUbo8rZaxfBvBDAF4AcDz43XEAL+yBsZ2EXOR/A8DvAyDIU4GlpOu8i+NcAvBdBMUHsd/vqWsK4ASASwAOAygF1/Rv7aVrCuAUgAt51xDArwB4NOl9uzHOmdf+NoDPB//esvYBPAXge3dznAC+BElKXgFwdDeu575i+IgWlsLl4Hd7CkR0CsB7AHwNwO1CiGvBS9cB3L5b44rhnwH4XwBMg5+PAGgJIcbBz3vlut4DYBXA/xfIT79KRE3ssWsqhLgC4Ochmd01AG0A38DevKYKaddwL6+x/x7AHwb/3lPjJKJHAFwRQnxr5qUdHed+c/h7HkS0AODfAfgpIcR6/DUht/hdrZMloh8G8IYQ4hu7OQ5NlAC8F8AvCSHeA6CDGflmj1zTFQCPQG5QdwJoIiHk36vYC9cwD0T0aUjZ9PO7PZZZEFEDwP8K4DO7PZb95vCvALgr9vPJ4Hd7AkRUhnT2nxdC/G7w69eJ6Hjw+nEAb+zW+AJ8H4APE9ErAB6HlHX+OYBlIioF79kr1/UygMtCiK8FP38JcgPYa9f0AwC+K4RYFUKMAPwu5HXei9dUIe0a7rk1RkQ/DuCHAfzdYHMC9tY43wa52X8rWFcnAfwVEd2BHR7nfnP4Xwdwb1D9UIFM2jyxy2MCILPxAP41gOeFEL8Qe+kJAB8N/v1RSG1/1yCE+JQQ4qQQ4hTk9ftTIcTfBfBVAD8SvG3XxwkAQojrAC4R0fcEv/pBAM9hj11TSCnn/UTUCOaBGueeu6YxpF3DJwD8WFBd8n4A7Zj0s+Mgooch5ccPCyG6sZeeAPARIqoS0T2QSdG/3I0xCiHOCyFuE0KcCtbVZQDvDebvzl7PnUpi7GCy5EOQ2frvAPj0bo8nNq7vhwyLzwH4ZvDfhyD18a8AeAnAnwA4vNtjjY35BwD8fvDvt0IumIsAfgdAdbfHF4zr3QDOBtf19wCs7MVrCuD/APBtABcA/AaA6l65pgC+AJlbGEE6o4+lXUPIBP7ngvV1HrLyaDfHeRFSA1dr6pdj7/90MM4XAHxwN8c58/oriJK2O3o9fWsFDw8PjwOC/SbpeHh4eHikwDt8Dw8PjwMC7/A9PDw8Dgi8w/fw8PA4IPAO38PDw+OAwDt8Dw8PjwMC7/A9PDw8Dgj+f+pMfZhgjpNOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#AUTOCORRELATION\n",
    "#Autocorrelation is simply the correlation of a series with its own lags.\n",
    "# Plot lag on x axis and correlation on y axis\n",
    "#Any correlation above confidence lnes are statistically significant.\n",
    "\n",
    "from statsmodels.tsa.stattools import acf\n",
    "acf_144 = acf(df_new.Rainfall, nlags=144)\n",
    "plt.plot(acf_144)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Lag', ylabel='Autocorrelation'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "#Obtain the same but with single line and more info..\n",
    "from pandas.plotting import autocorrelation_plot\n",
    "autocorrelation_plot(df_new.Rainfall)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, using pandas plotting, that horizontal bands indicate 95% and 99% (dashed) confidence bands, we can clearly anything above this we are highly confident that any lags below 5000(mm), it is all positively correlated and it is above 99 confidence level. So any lag before 5000 mm in this data set (that is monthly), it is a strong positive correlation which is useful information to apply forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}