Autocorrelation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b84879ff",
   "metadata": {},
   "source": [
    "## Practical - 5\n",
    "\n",
    "## Autocorrelation\n",
    "\n",
    "## Problem"
   ]
  },
  {
   "attachments": {
    "Prac-5.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c74c6cf4",
   "metadata": {},
   "source": [
    "![Prac-5.png](attachment:Prac-5.png)"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "538364b3",
   "metadata": {},
   "source": [
    "![Practical%205%20%281%29_html_340f304fde01c84f.gif](attachment:Practical%205%20%281%29_html_340f304fde01c84f.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2335470c",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2c6004eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.graphics import tsaplots\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bca1d0a1",
   "metadata": {},
   "source": [
    "## Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be36d4fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [9.08,12.63,15,20.73,2.2,18,7.16,18.28,21,19.68,15.54,\n",
    "       24,16.1,11.93,27,12.51,20.04,30,12.41,14.33,33,22.11,\n",
    "       17.91,36]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "706e0c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Original_Data':data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6899997b",
   "metadata": {},
   "outputs": [],
   "source": [
    "One_Unit_Lag = df.shift(1,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "676b7100",
   "metadata": {},
   "outputs": [],
   "source": [
    "Second_Unit_Lag = df.shift(2,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "14a28091",
   "metadata": {},
   "outputs": [],
   "source": [
    "Third_Unit_Lag = df.shift(3,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1ea136ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['1_Unit_Lag'] = One_Unit_Lag\n",
    "df['2_Unit_Lag'] = Second_Unit_Lag\n",
    "df['3_Unit_Lag'] = Third_Unit_Lag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "36024310",
   "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>Original_Data</th>\n",
       "      <th>1_Unit_Lag</th>\n",
       "      <th>2_Unit_Lag</th>\n",
       "      <th>3_Unit_Lag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.63</td>\n",
       "      <td>9.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.00</td>\n",
       "      <td>12.63</td>\n",
       "      <td>9.08</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.73</td>\n",
       "      <td>15.00</td>\n",
       "      <td>12.63</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.20</td>\n",
       "      <td>20.73</td>\n",
       "      <td>15.00</td>\n",
       "      <td>12.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.00</td>\n",
       "      <td>2.20</td>\n",
       "      <td>20.73</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.16</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2.20</td>\n",
       "      <td>20.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.28</td>\n",
       "      <td>7.16</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21.00</td>\n",
       "      <td>18.28</td>\n",
       "      <td>7.16</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19.68</td>\n",
       "      <td>21.00</td>\n",
       "      <td>18.28</td>\n",
       "      <td>7.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.54</td>\n",
       "      <td>19.68</td>\n",
       "      <td>21.00</td>\n",
       "      <td>18.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>24.00</td>\n",
       "      <td>15.54</td>\n",
       "      <td>19.68</td>\n",
       "      <td>21.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>16.10</td>\n",
       "      <td>24.00</td>\n",
       "      <td>15.54</td>\n",
       "      <td>19.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.93</td>\n",
       "      <td>16.10</td>\n",
       "      <td>24.00</td>\n",
       "      <td>15.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>27.00</td>\n",
       "      <td>11.93</td>\n",
       "      <td>16.10</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.51</td>\n",
       "      <td>27.00</td>\n",
       "      <td>11.93</td>\n",
       "      <td>16.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20.04</td>\n",
       "      <td>12.51</td>\n",
       "      <td>27.00</td>\n",
       "      <td>11.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30.00</td>\n",
       "      <td>20.04</td>\n",
       "      <td>12.51</td>\n",
       "      <td>27.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12.41</td>\n",
       "      <td>30.00</td>\n",
       "      <td>20.04</td>\n",
       "      <td>12.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14.33</td>\n",
       "      <td>12.41</td>\n",
       "      <td>30.00</td>\n",
       "      <td>20.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>33.00</td>\n",
       "      <td>14.33</td>\n",
       "      <td>12.41</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22.11</td>\n",
       "      <td>33.00</td>\n",
       "      <td>14.33</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17.91</td>\n",
       "      <td>22.11</td>\n",
       "      <td>33.00</td>\n",
       "      <td>14.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>36.00</td>\n",
       "      <td>17.91</td>\n",
       "      <td>22.11</td>\n",
       "      <td>33.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Original_Data  1_Unit_Lag  2_Unit_Lag  3_Unit_Lag\n",
       "0            9.08         NaN         NaN         NaN\n",
       "1           12.63        9.08         NaN         NaN\n",
       "2           15.00       12.63        9.08         NaN\n",
       "3           20.73       15.00       12.63        9.08\n",
       "4            2.20       20.73       15.00       12.63\n",
       "5           18.00        2.20       20.73       15.00\n",
       "6            7.16       18.00        2.20       20.73\n",
       "7           18.28        7.16       18.00        2.20\n",
       "8           21.00       18.28        7.16       18.00\n",
       "9           19.68       21.00       18.28        7.16\n",
       "10          15.54       19.68       21.00       18.28\n",
       "11          24.00       15.54       19.68       21.00\n",
       "12          16.10       24.00       15.54       19.68\n",
       "13          11.93       16.10       24.00       15.54\n",
       "14          27.00       11.93       16.10       24.00\n",
       "15          12.51       27.00       11.93       16.10\n",
       "16          20.04       12.51       27.00       11.93\n",
       "17          30.00       20.04       12.51       27.00\n",
       "18          12.41       30.00       20.04       12.51\n",
       "19          14.33       12.41       30.00       20.04\n",
       "20          33.00       14.33       12.41       30.00\n",
       "21          22.11       33.00       14.33       12.41\n",
       "22          17.91       22.11       33.00       14.33\n",
       "23          36.00       17.91       22.11       33.00"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "12a26bf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lag_no = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d30d2683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        , -0.0928397 ,  0.03872485,  0.44511117])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.tsa.acf(data,nlags=lag_no)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f5767335",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = tsaplots.plot_acf(data,lags=lag_no)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3073a57",
   "metadata": {},
   "source": [
    "## Autocorrelation for 3 lags is 0.44511117"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}