Climate_Change_timeseries_analysis_and_forecasting.ipynb

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    "colab": {
      "name": "Climate_Change_timeseries_analysis_and_forecasting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/markpairdha/climate-change-time-series-analysis/blob/main/Climate_Change_timeseries_analysis_and_forecasting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3FTlT0OGsdt",
        "outputId": "55a928d2-693a-4320-8553-e5327efd9dff"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6V3J8J4tamv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "6b5b87b3-d2d2-4501-d0e7-21f3d1444d87"
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/climate change time series/new_york_temperature.csv')\n",
        "df.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>TAVG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1870</td>\n",
              "      <td>53.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1871</td>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1872</td>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1873</td>\n",
              "      <td>50.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1874</td>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   DATE  TAVG\n",
              "0  1870  53.8\n",
              "1  1871  51.3\n",
              "2  1872  51.3\n",
              "3  1873  50.9\n",
              "4  1874  51.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "xhDuqjrfJ3UQ",
        "outputId": "ec67a0f4-ac5b-4a8d-9beb-ba5196eaa2b5"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>TAVG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>147.000000</td>\n",
              "      <td>147.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1943.000000</td>\n",
              "      <td>53.948980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>42.579338</td>\n",
              "      <td>1.666157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1870.000000</td>\n",
              "      <td>49.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1906.500000</td>\n",
              "      <td>52.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1943.000000</td>\n",
              "      <td>54.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1979.500000</td>\n",
              "      <td>55.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>57.300000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              DATE        TAVG\n",
              "count   147.000000  147.000000\n",
              "mean   1943.000000   53.948980\n",
              "std      42.579338    1.666157\n",
              "min    1870.000000   49.400000\n",
              "25%    1906.500000   52.950000\n",
              "50%    1943.000000   54.000000\n",
              "75%    1979.500000   55.050000\n",
              "max    2016.000000   57.300000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5bqlgdgt0Zy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "02c61a08-6f43-47ea-c175-1cf8b47ec446"
      },
      "source": [
        "#Code To Check \"Is Tempearture a Random Walk\"\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df.set_index('DATE',inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TAVG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATE</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1870</th>\n",
              "      <td>53.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1871</th>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1872</th>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1873</th>\n",
              "      <td>50.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1874</th>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      TAVG\n",
              "DATE      \n",
              "1870  53.8\n",
              "1871  51.3\n",
              "1872  51.3\n",
              "1873  50.9\n",
              "1874  51.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5lPAtEmvFGg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "1d199edc-8363-4600-c54d-660385195ac4"
      },
      "source": [
        "df.index=pd.to_datetime(df.index,format='%Y')\n",
        "df.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TAVG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATE</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1870-01-01</th>\n",
              "      <td>53.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1871-01-01</th>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1872-01-01</th>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1873-01-01</th>\n",
              "      <td>50.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1874-01-01</th>\n",
              "      <td>51.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            TAVG\n",
              "DATE            \n",
              "1870-01-01  53.8\n",
              "1871-01-01  51.3\n",
              "1872-01-01  51.3\n",
              "1873-01-01  50.9\n",
              "1874-01-01  51.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3AsgAhKwdKk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "2352a9b7-38db-4c99-c146-99ee1a11c416"
      },
      "source": [
        "df.plot()\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osRwICJ7xQgq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c4cee84-0525-4d8c-b8c2-394b296904ed"
      },
      "source": [
        "#Applying Dicky Fuller Test to test whether the temperature is Random walk or not\n",
        "\n",
        "result=adfuller(df['TAVG'])\n",
        "print(\"The P Value for the ADF test is : \",result[1])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The P Value for the ADF test is :  0.5832938987871142\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTJNXFFGyK5s"
      },
      "source": [
        "***The P value for the ADF test is 0.58 which is greater than 0.5. Therefore, Temperature is a Random Walk.***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5e4EDLRpx-SK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "outputId": "f2de936f-ce2f-4edf-b3ed-4af5462de76a"
      },
      "source": [
        "#Making the temperature stationary.\n",
        "\n",
        "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
        "temp_change=df.diff()\n",
        "temp_change.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TAVG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATE</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1870-01-01</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1871-01-01</th>\n",
              "      <td>-2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1872-01-01</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1873-01-01</th>\n",
              "      <td>-0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1874-01-01</th>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            TAVG\n",
              "DATE            \n",
              "1870-01-01   NaN\n",
              "1871-01-01  -2.5\n",
              "1872-01-01   0.0\n",
              "1873-01-01  -0.4\n",
              "1874-01-01   0.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2O8ZyppzHEl"
      },
      "source": [
        "#Dropping the first row\n",
        "\n",
        "temp_change=temp_change.dropna()"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcHwxor8zNr8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "82550e25-d3b9-4eca-c65f-67af0fe4ddb3"
      },
      "source": [
        "#Plotting the Autocorrelation Function\n",
        "\n",
        "plot_acf(temp_change,lags=20)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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OUbbueoHePgd8q1i5aBZnzZ3B5qeehfYOpp3QwVlzZ7By0ayyu2bWUvyXtjbptbWJ6y5azowt32T64z/kP/7mMh+wNRsDB761hLY20blvO9Of/BHnLTnVYW82Bg58M7NEOPDNzBLhwDczS4QD38wsERN6aQUzs9RVq8GmHft5Yt9Blp5+EisXzZqwkxAc+GZWqjIDcKKVfV0oB34LS+mNUhZv4/FVdgBOtLKvC+XAb1GpvVHKkOI2nugPuLIDcKKVfV0oB36LSu2NUoa827jVvh2U8QFXdgBOtLKvC+XAb1GpvVHKkGcbt+K3gzIGEWUH4EQr+7pQPi2zRfW/UepN5TdKGfJs4/rwDAaG52Q11AfceOkPQPp6IapMyz4Yp+qF8cq+LpQDv0Wl9kYpQ55tXEZ45lXGIKLsACxDmdeFcuC3qBTfKBMtzzZuxW9gZQ0ifGG8iePAb2F+o4y/sW7jVvwG5kHE1OfANxsHrRqeKQ0iqtXgoSef5xsP9fDQk89TrU79H0XyWToFaLXT72xi9Icn+7Zz3pKPlt0dq9OKZ1EVoZDAl7QG+Ctqv2n7dxFxU8P6acCXgFcD+4B3RMQTRbRdtlR3HLNWlurfseSe0pHUDtwMXASsAC6XtKKh2lXA8xFxFvBZ4C/ytjtZtOLpd2VK8Wv0WHg7ja9WPIuqCIrItyNJei3wyYj47Wz5YwAR8ed1de7O6vxYUgfwDDAnhmj8tCXL483X3TKmPm16eBMAK89Z2XT9gSN9VHO+7n57XzzCswd6jyufM6OT2SdPK6SNoTy65WcALFvx6+PeVl4RwVPPHebw0QoRIMH0E9pZfNp0pOG/DZX1WvO0O5bH5t1OeZWxnSe6zRdf6mPn/sPUx4AEC2ZN5+SXjf9M93Cvd+bLThjzc9/1/n/zYER0NVtXxCtbAOyoW+4BVg1WJyL6JP0SOB14tr6SpLXAWoAZZ7xyzB0aLOhHYrQ73stOaEfiuB1n2gnt49ZmvTxvkIkOsgNHKsdCDGrb7PDRCgeOVEb0JivrteZpdyyPzbudoJzX20r78Yxp7Uw/of24D9UZ0yb/+zaPIkb4bwfWRMTV2fJ7gFURcU1dnZ9ldXqy5V9kdZ5t9pwAXV1d0d3dnatvg3nwyefo7Wv+uj/wzosBuPkr60f0XEXM4Y+2zaLkaXcsj/3GQz18/cEe6re8gLe/eiFvO2/hqPswGmVt47EoYjuV8XrLaLNaDX7/Qx+hMmMe17x/7ahOmMh7ssV4vt5VZ5425mOAksZ1hL8TWFS3vDAra1anJ5vSOYXawduW13/6nc/SGV5q100ZK2+nkekfbB1Y8VZo7+Dz3390VIOttjZx3pJTp/RB2kZFnIf/ALBM0pmSOoHLgMaPvPXAFdn9twPfH2r+vtX07zhvO2/hlD93GWpvtN7Tz+LwkteN6oBi/x8jTetoQ9ASf4xUBm+nkek/YYKOTlCbT5gYgdwj/GxO/hrgbmqnZd4SEZsl3Qh0R8R64IvA7ZK2A89R+1CwFpRnVOVvQyPj7TQyvmLs6BVyODoiNgAbGspuqLv/EvDvi2hrqukfLVdmzOOhJ5+f9G/sAaMqRn/+copfo8ciz3ZqtX1qrDz1NXq+tEKJ6kfLh898A5///qN86ttbJ+Sc67FOy6R6/nKrKHOfmmhlTn2N9f1TNl9aoUR5R8tjlWdaxqOqya2sfaoMZU195T1YXCaP8EtU1mg5z8EuH1Cc3FL7BlbGCROtfLDYI/wSlTVaznOwywcUJzd/Axt/rXyw2CP8EpU1Ws774xypnYbaSsrap1p1TnssWvHHbfp5hF+iskbL/aHQ+NfBnpZpfWXsU608pz0Wrfz+ceCXrIzTFD0tM7VN9D6V0oFiaO33jwM/UT4f3orSynPaY9Wq7x/P4ZtZLq08p50aB76Z5eJTdVuHp3TMLJdWntNOjQPfzHJr1Tnt1HhKx8wsEQ58M7NEOPDNzBKRaOD7YJKZpSfJg7bnLZ7F4aMVDh6pcKi379jt0crUvf6HmVmSgS+JEzs7OLGzA5h2rPxIX4UTOzuoRHDaSZ0c7O3jyNHq4E9kZtZCcgW+pNOArwJLgSeASyPi+YY6K4EvADOBCvBnEfHVPO2Ol2kd7XS0iw7E2S8/GYC+SpWDvQO/CRzqrTB1foJ96kvlJ//MhpN3hH8tsDEibpJ0bbb80YY6h4D3RsSjkuYDD0q6OyIm/68FAB3tbZwyvY1Tpp9wrKxajdqUUG8fh45kt70V+jwlNOmkdiVHs6HkDfxLgNXZ/duAe2gI/Ij4ed39pyXtAeYALRH4zbS1iZOmdXDStA44+VflvX1VgmJCf7hvEPXr+9scWNZfLwYsD6g3wueof57G8qH6Oui2yLmJKhFUoxbm1Qgq1Ww5frUcAZVqcP/j+/hFQldyNBtK3sCfFxG7svvPAPOGqizpfKAT+MUg69cCawEWL16cs2sTr/ECUla+//PzvQN+/QlqH8y9lSrnLDqFavbBUK1G7YMku62Vcez+UB+c9R9sx8qarKsvH0zj+vEaQBz/oR2Drh+4avDXEw1lEXFcmZVr2MCX9D3g5U1WXV+/EBEhadD/VklnALcDV0RE0yOhEbEOWAfQ1dXlXcRye9X8mUzvbOdQb+VY2fTOdv71wlOyg/Y2UY59aMbAb6DHfVA0+bY51PpmQdH4ATZemrc9fMVmH+T1j9M4zTYOu8dHxIWDrZO0W9IZEbErC/Q9g9SbCfwzcH1E3Dfm3pqN0uqz57Jy0Sw27djP4d4K0zvbWbloFqvPnlt215KjLMUGhpmPo0ykvEOc9cAVwE3Z7bcaK0jqBP4J+FJEfD1ne2aj0t4mbr9qFfds28OWp19gxfyZrD57Lu0+YGsJyhv4NwF3SboKeBK4FEBSF/D+iLg6K3sjcLqkK7PHXRkRm3K2bTYi7W3iguXzuGD5kIeYzKa8XIEfEfuAC5qUdwNXZ/e/DHw5TztmZpafTysxM0uEA9/MLBEOfDOzRDjwzcwS4cA3M0uEA9/MLBEOfDOzRDjwzcwS4cA3M0uEA9/MLBEOfDOzRDjwzcwS4cA3M0uEA9/MLBEOfDOzRDjwzcwS4cA3M0uEA9/MLBG5Al/SaZK+K+nR7PbUIerOlNQj6a/ztGlmZmOTd4R/LbAxIpYBG7PlwfwJcG/O9szMbIzyBv4lwG3Z/duAtzarJOnVwDzgOznbMzOzMcob+PMiYld2/xlqoT6ApDbg08AfDfdkktZK6pbUvXfv3pxdMzOzeh3DVZD0PeDlTVZdX78QESEpmtT7A2BDRPRIGrKtiFgHrAPo6upq9lxmZjZGwwZ+RFw42DpJuyWdERG7JJ0B7GlS7bXAGyT9ATAD6JR0ICKGmu83M7OCDRv4w1gPXAHclN1+q7FCRLyr/76kK4Euh72Z2cTLO4d/E/BmSY8CF2bLSOqS9Hd5O2dmZsXJNcKPiH3ABU3Ku4Grm5TfCtyap83xVKkGh2a9gt6T5rFx625Wnz2X9rahjzuYmbWKvFM6U0alGrzni/ezd9nvEm0dfPCOn7By0Sxuv2qVQ9/MpgRfWiFzz7Y9bNqxn2jvBLVxqLfCph37uWdbs+PQZmatx4Gf2fz0CxzurQwoO9xbYcvTL5TUIzOzYjnwM6+aP5Ppne0DyqZ3trNi/sySemRmViwHfmb12XNZuWgWJ3a2I+DEznZWLprF6rPnlt01M7NC+KBtpr1N3H7VKu7ZtoctT7/AivkzfZaOmU0pDvw67W3iguXzuGD5cZcEMjNreZ7SMTNLhAPfzCwRDnwzs0Q48M3MEuHANzNLhCIm5++MSNoLPJnjKWYDzxbUnanM22lkvJ1Gxttp5MZrWy2JiDnNVkzawM9LUndEdJXdj8nO22lkvJ1Gxttp5MrYVp7SMTNLhAPfzCwRUznw15XdgRbh7TQy3k4j4+00chO+rabsHL6ZmQ00lUf4ZmZWx4FvZpaIKRf4ktZI2iZpu6Rry+7PZCbpCUk/lbRJUnfZ/ZksJN0iaY+kn9WVnSbpu5IezW5PLbOPk8Eg2+mTknZm+9QmSW8ps4+TgaRFkn4gaYukzZI+lJVP+D41pQJfUjtwM3ARsAK4XNKKcns16f1GRKz0udMD3AqsaSi7FtgYEcuAjdly6m7l+O0E8Nlsn1oZERsmuE+TUR/wkYhYAbwG+ECWSxO+T02pwAfOB7ZHxGMR0QvcCVxScp+sxUTEvcBzDcWXALdl928D3jqhnZqEBtlO1iAidkXEQ9n9F4GtwAJK2KemWuAvAHbULfdkZdZcAN+R9KCktWV3ZpKbFxG7svvPAP6VnMFdI+mRbMon+amvepKWAucC91PCPjXVAt9G5/URcR61KbAPSHpj2R1qBVE7l9nnMzf3BeCVwEpgF/DpcrszeUiaAfwj8OGIeKF+3UTtU1Mt8HcCi+qWF2Zl1kRE7Mxu9wD/RG1KzJrbLekMgOx2T8n9mZQiYndEVCKiCvwt3qcAkHQCtbD/h4j4RlY84fvUVAv8B4Blks6U1AlcBqwvuU+TkqSTJJ3cfx/4LeBnQz8qaeuBK7L7VwDfKrEvk1Z/gGX+Ld6nkCTgi8DWiPhM3aoJ36em3F/aZqeBfQ5oB26JiD8ruUuTkqRXUBvVQ+3H7L/ibVUj6Q5gNbXL1+4GPgF8E7gLWEztst2XRkTSBywH2U6rqU3nBPAE8Pt189RJkvR64IfAT4FqVnwdtXn8Cd2nplzgm5lZc1NtSsfMzAbhwDczS4QD38wsEQ58M7NEOPDNzBLhwDczS4QD38wsEf8fAJUBDMZGIGgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuBiaB0DzoOe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "3490a80b-4af9-4435-fab0-21ecf1257bba"
      },
      "source": [
        "#Plotting the Partial Autocorrelation Function\n",
        "\n",
        "plot_pacf(temp_change,lags=20)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_vg7VdYzyqQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "11dfa10c-6397-4140-d1d2-4f805e2ba974"
      },
      "source": [
        "#Finding which ARMA Model is best\n",
        "#AR(1) Model\n",
        "from statsmodels.tsa.arima_model import ARMA\n",
        "\n",
        "mod=ARMA(temp_change,order=(1,0))\n",
        "res=mod.fit()\n",
        "print(\"The AIC for AR(1) is : \", res.aic)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The AIC for AR(1) is :  510.534689831391\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ldbk4Tj5CE5f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1268d82e-7e31-47a4-8524-82f63ff7c9d6"
      },
      "source": [
        "#AR(2) Model \n",
        "\n",
        "mod1=ARMA(temp_change,order=(2,0))\n",
        "res1=mod1.fit()\n",
        "print(\"The AIC for AR(2) is : \",res1.aic)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The AIC for AR(2) is :  501.9274123160228\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reRUJL0BCqGT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d55778e-41cc-4144-e271-e138ad8b4594"
      },
      "source": [
        "#MA(1) Model\n",
        "\n",
        "mod2=ARMA(temp_change, order=(0,1))\n",
        "res2=mod2.fit()\n",
        "print(\"The AIC for MA(1) is : \",res2.aic)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The AIC for MA(1) is :  469.3909708062573\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DuSwZrxjC6Qu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "010f55db-a80a-42d4-9e1a-5ee9f65cbf57"
      },
      "source": [
        "#ARMA(1,1) Model\n",
        "\n",
        "mod3=ARMA(temp_change,order=(1,1))\n",
        "res3=mod3.fit()\n",
        "print(\"The AIC for ARMA(1,1) is : \",res3.aic)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The AIC for ARMA(1,1) is :  469.0729131153337\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oFxBIfiXDhKB"
      },
      "source": [
        "**The MA(1) and ARMA(1,1) have the two lowest AIC values.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeHjsyCxDjMQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "b423cf1b-a726-4bc1-ed23-ea3af6dbc077"
      },
      "source": [
        "#We will be using ARMA(1,1) model to forecast temperature \n",
        "\n",
        "mod4=ARMA(df,order=(1,1,1))\n",
        "res4=mod4.fit()\n",
        "res4.plot_predict(start='1872-01-01',end='2046-01-01')\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency AS-JAN will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odj81u7BGP5T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f76e78fd-6c8e-4560-a927-290b64273b13"
      },
      "source": [
        "y_pred=res4.predict(start='1872-01-01',end='2046-01-01')\n",
        "y_pred"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1872-01-01    52.790149\n",
              "1873-01-01    52.344585\n",
              "1874-01-01    51.996791\n",
              "1875-01-01    51.859788\n",
              "1876-01-01    51.391903\n",
              "                ...    \n",
              "2042-01-01    55.558986\n",
              "2043-01-01    55.547466\n",
              "2044-01-01    55.536028\n",
              "2045-01-01    55.524670\n",
              "2046-01-01    55.513393\n",
              "Freq: AS-JAN, Length: 175, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vk-YrwiiGaKw"
      },
      "source": [
        ""
      ],
      "execution_count": 35,
      "outputs": []
    }
  ]
}