ARIMA&SARIMA_Time_Series.ipynb

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        "<a href=\"https://colab.research.google.com/github/gulabpatel/StockPricePrediction/blob/master/ARIMA%26SARIMA_Time_Series.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "\n",
        "# ARIMA and Seasonal ARIMA\n",
        "\n",
        "\n",
        "## Autoregressive Integrated Moving Averages\n",
        "\n",
        "The general process for ARIMA models is the following:\n",
        "* Visualize the Time Series Data\n",
        "* Make the time series data stationary\n",
        "* Plot the Correlation and AutoCorrelation Charts\n",
        "* Construct the ARIMA Model or Seasonal ARIMA based on the data\n",
        "* Use the model to make predictions\n",
        "\n",
        "Let's go through these steps!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mew50wwmlFE2"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHxurbi0lFE3"
      },
      "source": [
        "df=pd.read_csv('/content/perrin-freres-monthly-champagne-.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
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        },
        "outputId": "d899d8f2-f8ea-4044-f33d-a95d9787dbc8"
      },
      "source": [
        "df.sample(10)"
      ],
      "execution_count": 5,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>1970-06</td>\n",
              "      <td>3986.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>1971-09</td>\n",
              "      <td>5951.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1965-08</td>\n",
              "      <td>1759.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>1968-12</td>\n",
              "      <td>11331.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>1968-10</td>\n",
              "      <td>6922.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1965-02</td>\n",
              "      <td>2475.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>1972-07</td>\n",
              "      <td>4298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1967-09</td>\n",
              "      <td>4739.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>1969-06</td>\n",
              "      <td>4677.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>1971-11</td>\n",
              "      <td>9851.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "       Month  Perrin Freres monthly champagne sales millions ?64-?72\n",
              "77   1970-06                                             3986.0     \n",
              "92   1971-09                                             5951.0     \n",
              "19   1965-08                                             1759.0     \n",
              "59   1968-12                                            11331.0     \n",
              "57   1968-10                                             6922.0     \n",
              "13   1965-02                                             2475.0     \n",
              "102  1972-07                                             4298.0     \n",
              "44   1967-09                                             4739.0     \n",
              "65   1969-06                                             4677.0     \n",
              "94   1971-11                                             9851.0     "
            ]
          },
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      "source": [
        "df.tail()"
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              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Perrin Freres monthly champagne sales millions ?64-?72</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>1972-07</td>\n",
              "      <td>4298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>1972-08</td>\n",
              "      <td>1413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1972-09</td>\n",
              "      <td>5877.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>Perrin Freres monthly champagne sales millions...</td>\n",
              "      <td>NaN</td>\n",
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            "text/plain": [
              "                                                 Month  Perrin Freres monthly champagne sales millions ?64-?72\n",
              "102                                            1972-07                                             4298.0     \n",
              "103                                            1972-08                                             1413.0     \n",
              "104                                            1972-09                                             5877.0     \n",
              "105                                                NaN                                                NaN     \n",
              "106  Perrin Freres monthly champagne sales millions...                                                NaN     "
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 4
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          "height": 204
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        "outputId": "8a8dc01f-23c4-420d-a40c-e8b2dce2d162"
      },
      "source": [
        "## Cleaning up the data\n",
        "df.columns=[\"Month\",\"Sales\"]\n",
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1964-01</td>\n",
              "      <td>2815.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1964-02</td>\n",
              "      <td>2672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1964-03</td>\n",
              "      <td>2755.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1964-04</td>\n",
              "      <td>2721.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1964-05</td>\n",
              "      <td>2946.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "     Month   Sales\n",
              "0  1964-01  2815.0\n",
              "1  1964-02  2672.0\n",
              "2  1964-03  2755.0\n",
              "3  1964-04  2721.0\n",
              "4  1964-05  2946.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxFFW9EKlFE5"
      },
      "source": [
        "## Drop last 2 rows\n",
        "df.drop(106,axis=0,inplace=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-ago-GflFE6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "8ca364ea-79ce-470c-e3c3-989b4994b3c1"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>1972-06</td>\n",
              "      <td>5312.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>1972-07</td>\n",
              "      <td>4298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>1972-08</td>\n",
              "      <td>1413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1972-09</td>\n",
              "      <td>5877.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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            "text/plain": [
              "       Month   Sales\n",
              "101  1972-06  5312.0\n",
              "102  1972-07  4298.0\n",
              "103  1972-08  1413.0\n",
              "104  1972-09  5877.0\n",
              "105      NaN     NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "JHPVb5NClFE6"
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      "source": [
        "df.drop(105,axis=0,inplace=True)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTh_kWlHlFE6",
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          "height": 204
        },
        "outputId": "8b1c3bad-0aaa-4418-ea33-cce14e93d3f6"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>1972-05</td>\n",
              "      <td>4618.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>1972-06</td>\n",
              "      <td>5312.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>1972-07</td>\n",
              "      <td>4298.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>1972-08</td>\n",
              "      <td>1413.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>1972-09</td>\n",
              "      <td>5877.0</td>\n",
              "    </tr>\n",
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            ],
            "text/plain": [
              "       Month   Sales\n",
              "100  1972-05  4618.0\n",
              "101  1972-06  5312.0\n",
              "102  1972-07  4298.0\n",
              "103  1972-08  1413.0\n",
              "104  1972-09  5877.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehsWBTmtlFE6"
      },
      "source": [
        "# Convert Month into Datetime\n",
        "df['Month']=pd.to_datetime(df['Month'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "outputId": "7e93fead-0a12-40aa-bb5c-a9e4b878cf92"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1964-01-01</td>\n",
              "      <td>2815.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1964-02-01</td>\n",
              "      <td>2672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1964-03-01</td>\n",
              "      <td>2755.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1964-04-01</td>\n",
              "      <td>2721.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1964-05-01</td>\n",
              "      <td>2946.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "       Month   Sales\n",
              "0 1964-01-01  2815.0\n",
              "1 1964-02-01  2672.0\n",
              "2 1964-03-01  2755.0\n",
              "3 1964-04-01  2721.0\n",
              "4 1964-05-01  2946.0"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 12
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    {
      "cell_type": "code",
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        "id": "v8JE02JalFE7"
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      "source": [
        "df.set_index('Month',inplace=True)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0c-2S92lFE7",
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          "height": 235
        },
        "outputId": "95938f12-8ecc-4431-caa1-202a1e104995"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>1964-01-01</th>\n",
              "      <td>2815.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-02-01</th>\n",
              "      <td>2672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-03-01</th>\n",
              "      <td>2755.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-04-01</th>\n",
              "      <td>2721.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-05-01</th>\n",
              "      <td>2946.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Sales\n",
              "Month             \n",
              "1964-01-01  2815.0\n",
              "1964-02-01  2672.0\n",
              "1964-03-01  2755.0\n",
              "1964-04-01  2721.0\n",
              "1964-05-01  2946.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYRhpFGOlFE8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "8b3e6fee-d226-43b2-d926-e90df7213293"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 15,
      "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>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>105.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4761.152381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2553.502601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1413.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3113.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4217.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5221.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>13916.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Sales\n",
              "count    105.000000\n",
              "mean    4761.152381\n",
              "std     2553.502601\n",
              "min     1413.000000\n",
              "25%     3113.000000\n",
              "50%     4217.000000\n",
              "75%     5221.000000\n",
              "max    13916.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJEiVTqzlFE8"
      },
      "source": [
        "## Step 2: Visualize the Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWtAcdUmlFE8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "b04030e6-8cb1-4828-a247-224384b459f4"
      },
      "source": [
        "df.plot()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa4b5b0fda0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "hxn4GRYvPQLH"
      },
      "source": [
        "#Stationary Check"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzjguIJolFE8"
      },
      "source": [
        "### Testing For Stationarity\n",
        "\n",
        "from statsmodels.tsa.stattools import adfuller"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62A80O35lFE8"
      },
      "source": [
        "test_result=adfuller(df['Sales'])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AlAvsdXGlFE8"
      },
      "source": [
        "#Ho: It is non stationary\n",
        "#H1: It is stationary\n",
        "\n",
        "def adfuller_test(sales):\n",
        "    result=adfuller(sales)\n",
        "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
        "    for value,label in zip(result,labels):\n",
        "        print(label+' : '+str(value) )\n",
        "    if result[1] <= 0.05:\n",
        "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
        "    else:\n",
        "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")\n",
        "    "
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntddzcY8lFE8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "527884c6-c8d2-4919-ce17-44ef0314f7da"
      },
      "source": [
        "adfuller_test(df['Sales'])"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ADF Test Statistic : -1.8335930563276193\n",
            "p-value : 0.36391577166024686\n",
            "#Lags Used : 11\n",
            "Number of Observations Used : 93\n",
            "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4p3WJTPlFE9"
      },
      "source": [
        "## Differencing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TC2T_zOelFE9"
      },
      "source": [
        "df['Sales First Difference'] = df['Sales'] - df['Sales'].shift(1)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2xG_cEWlFE9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65ac14cc-c1d0-4211-e652-5c6b09400351"
      },
      "source": [
        "df['Sales'].shift(1)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "1964-01-01       NaN\n",
              "1964-02-01    2815.0\n",
              "1964-03-01    2672.0\n",
              "1964-04-01    2755.0\n",
              "1964-05-01    2721.0\n",
              "               ...  \n",
              "1972-05-01    4788.0\n",
              "1972-06-01    4618.0\n",
              "1972-07-01    5312.0\n",
              "1972-08-01    4298.0\n",
              "1972-09-01    1413.0\n",
              "Name: Sales, Length: 105, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXR6tRtBlFE9"
      },
      "source": [
        "#Shifting by 12 because of seasonality\n",
        "df['Seasonal First Difference']=df['Sales']-df['Sales'].shift(12)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGPDky_llFE9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "outputId": "f0c89b20-5d03-405b-8379-5ca34934adae"
      },
      "source": [
        "df.head(14)"
      ],
      "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>Sales</th>\n",
              "      <th>Sales First Difference</th>\n",
              "      <th>Seasonal First Difference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1964-01-01</th>\n",
              "      <td>2815.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-02-01</th>\n",
              "      <td>2672.0</td>\n",
              "      <td>-143.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-03-01</th>\n",
              "      <td>2755.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-04-01</th>\n",
              "      <td>2721.0</td>\n",
              "      <td>-34.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-05-01</th>\n",
              "      <td>2946.0</td>\n",
              "      <td>225.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-06-01</th>\n",
              "      <td>3036.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-07-01</th>\n",
              "      <td>2282.0</td>\n",
              "      <td>-754.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-08-01</th>\n",
              "      <td>2212.0</td>\n",
              "      <td>-70.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-09-01</th>\n",
              "      <td>2922.0</td>\n",
              "      <td>710.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-10-01</th>\n",
              "      <td>4301.0</td>\n",
              "      <td>1379.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-11-01</th>\n",
              "      <td>5764.0</td>\n",
              "      <td>1463.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-12-01</th>\n",
              "      <td>7312.0</td>\n",
              "      <td>1548.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1965-01-01</th>\n",
              "      <td>2541.0</td>\n",
              "      <td>-4771.0</td>\n",
              "      <td>-274.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1965-02-01</th>\n",
              "      <td>2475.0</td>\n",
              "      <td>-66.0</td>\n",
              "      <td>-197.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Sales  Sales First Difference  Seasonal First Difference\n",
              "Month                                                                \n",
              "1964-01-01  2815.0                     NaN                        NaN\n",
              "1964-02-01  2672.0                  -143.0                        NaN\n",
              "1964-03-01  2755.0                    83.0                        NaN\n",
              "1964-04-01  2721.0                   -34.0                        NaN\n",
              "1964-05-01  2946.0                   225.0                        NaN\n",
              "1964-06-01  3036.0                    90.0                        NaN\n",
              "1964-07-01  2282.0                  -754.0                        NaN\n",
              "1964-08-01  2212.0                   -70.0                        NaN\n",
              "1964-09-01  2922.0                   710.0                        NaN\n",
              "1964-10-01  4301.0                  1379.0                        NaN\n",
              "1964-11-01  5764.0                  1463.0                        NaN\n",
              "1964-12-01  7312.0                  1548.0                        NaN\n",
              "1965-01-01  2541.0                 -4771.0                     -274.0\n",
              "1965-02-01  2475.0                   -66.0                     -197.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IhJ7Y1BlFE9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e00761d-72ca-40d3-e11a-f2a318d1ea4c"
      },
      "source": [
        "## Again test dickey fuller test\n",
        "adfuller_test(df['Seasonal First Difference'].dropna())"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ADF Test Statistic : -7.626619157213166\n",
            "p-value : 2.0605796968136632e-11\n",
            "#Lags Used : 0\n",
            "Number of Observations Used : 92\n",
            "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JD8Cis2TlFE9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "ac7cff19-6b24-46c5-bdcf-902294b5db68"
      },
      "source": [
        "df['Seasonal First Difference'].plot()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa4aa964ac8>"
            ]
          },
          "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": "markdown",
      "metadata": {
        "id": "8VSdlcfwlFE9"
      },
      "source": [
        "## Auto Regressive Model\n",
        "\n",
        "```\n",
        "yt=c+ϕ1y(t−1)+ϕ2y(t−2)+⋯+ϕpy(t−p)+εt\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UL9R_qsYlFE9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "6bed1832-7489-4275-ec39-27232a7330f6"
      },
      "source": [
        "from pandas.plotting import autocorrelation_plot\n",
        "autocorrelation_plot(df['Sales'])\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "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": "markdown",
      "metadata": {
        "id": "-m5pgehllFE-"
      },
      "source": [
        "### Final Thoughts on Autocorrelation and Partial Autocorrelation\n",
        "\n",
        "* Identification of an AR model is often best done with the PACF.\n",
        "    * For an AR model, the theoretical PACF “shuts off” past the order of the model.  The phrase “shuts off” means that in theory the partial autocorrelations are equal to 0 beyond that point.  Put another way, the number of non-zero partial autocorrelations gives the order of the AR model.  By the “order of the model” we mean the most extreme lag of x that is used as a predictor.\n",
        "    \n",
        "    \n",
        "* Identification of an MA model is often best done with the ACF rather than the PACF.\n",
        "    * For an MA model, the theoretical PACF does not shut off, but instead tapers toward 0 in some manner.  A clearer pattern for an MA model is in the ACF.  The ACF will have non-zero autocorrelations only at lags involved in the model.\n",
        "    \n",
        "    p,d,q\n",
        "    p AR model lags\n",
        "    d differencing\n",
        "    q MA lags"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuXoKWsulFE-"
      },
      "source": [
        "import statsmodels as sm\n",
        "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8tOakhtlFE-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "f94b9308-b9b7-47cf-cd18-d8f30ddae0c7"
      },
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "ax1 = fig.add_subplot(211)\n",
        "fig = sm.graphics.tsaplots.plot_acf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax1)\n",
        "ax2 = fig.add_subplot(212)\n",
        "fig = sm.graphics.tsaplots.plot_pacf(df['Seasonal First Difference'].iloc[13:],lags=40,ax=ax2)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "df2TozYhapPQ"
      },
      "source": [
        "#SARIMA Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRxffbwFlFE-"
      },
      "source": [
        "# For non-seasonal data\n",
        "#p=1, d=1, q=0 or 1\n",
        "from statsmodels.tsa.arima_model import ARIMA"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OR30Oi6WP5tz",
        "outputId": "da7c812b-0694-4cfc-80d8-ee2095f3e32d"
      },
      "source": [
        "model = ARIMA(df['Sales'], order=(1,1,1))\n",
        "model_fit = model.fit()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  % freq, ValueWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jSRWzrWOlFE-"
      },
      "source": [
        "model=ARIMA(df['Sales'],order=(1,1,1))\n",
        "model_fit=model.fit()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4n4cN8UlFE-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "243cc8ae-e78d-4a70-e87c-19c82a402a8b"
      },
      "source": [
        "model_fit.summary()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARIMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>      <td>D.Sales</td>     <th>  No. Observations:  </th>    <td>104</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>          <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>-951.126</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th> <td>2227.262</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>          <td>Mon, 23 Nov 2020</td> <th>  AIC                </th> <td>1910.251</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>              <td>03:58:16</td>     <th>  BIC                </th> <td>1920.829</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>           <td>02-01-1964</td>    <th>  HQIC               </th> <td>1914.536</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                 <td>- 09-01-1972</td>   <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>         <td>   22.7846</td> <td>   12.405</td> <td>    1.837</td> <td> 0.069</td> <td>   -1.529</td> <td>   47.098</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1.D.Sales</th> <td>    0.4343</td> <td>    0.089</td> <td>    4.866</td> <td> 0.000</td> <td>    0.259</td> <td>    0.609</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.D.Sales</th> <td>   -1.0000</td> <td>    0.026</td> <td>  -38.503</td> <td> 0.000</td> <td>   -1.051</td> <td>   -0.949</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.1</th> <td>           2.3023</td> <td>          +0.0000j</td> <td>           2.3023</td> <td>           0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>           1.0000</td> <td>          +0.0000j</td> <td>           1.0000</td> <td>           0.0000</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                             ARIMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:                D.Sales   No. Observations:                  104\n",
              "Model:                 ARIMA(1, 1, 1)   Log Likelihood                -951.126\n",
              "Method:                       css-mle   S.D. of innovations           2227.262\n",
              "Date:                Mon, 23 Nov 2020   AIC                           1910.251\n",
              "Time:                        03:58:16   BIC                           1920.829\n",
              "Sample:                    02-01-1964   HQIC                          1914.536\n",
              "                         - 09-01-1972                                         \n",
              "=================================================================================\n",
              "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
              "---------------------------------------------------------------------------------\n",
              "const            22.7846     12.405      1.837      0.069      -1.529      47.098\n",
              "ar.L1.D.Sales     0.4343      0.089      4.866      0.000       0.259       0.609\n",
              "ma.L1.D.Sales    -1.0000      0.026    -38.503      0.000      -1.051      -0.949\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "AR.1            2.3023           +0.0000j            2.3023            0.0000\n",
              "MA.1            1.0000           +0.0000j            1.0000            0.0000\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFDERXHNlFE-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "67c5ecc8-ad52-4b40-ead4-55ae75c16658"
      },
      "source": [
        "df['forecast'] = model_fit.predict(start=90,end=103,dynamic=True)\n",
        "df[['Sales','forecast']].plot(figsize=(12,8))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa49ee5ed30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZKwuAyIdxl_H"
      },
      "source": [
        "We can see ARIMA model is working at all because data is seasonal so let's use advance models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z753-6tyyraz"
      },
      "source": [
        "#Decompostion - Trend, Seasonality and Residuals"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DP9ewb9RyQKd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "958103ba-b857-44c8-8d7c-45891259e664"
      },
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "ts_decompose = seasonal_decompose(df['Sales'], model='additive')\n",
        "ts_decompose.plot()\n",
        "plt.show()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BjGjCSrCahkb"
      },
      "source": [
        "#SARIMAX Model\n",
        "\n",
        "SARIMA - Seasonal Auto Regressive Integrated Moving Average with exogenous factors\n",
        "\n",
        "S - Seasonal,<br>\n",
        "A - Auto,<br>\n",
        "R - Reressive,<br>\n",
        "M - Moving,<br>\n",
        "A - Average,<br>\n",
        "X - Exogenous Variable.<br>\n",
        "\n",
        "X - A factor in the model whose value is identified by the factors of variables outside the model of study. e.g. In farming, production forecast is based on factors like area of land, quality of seed etc. but in this, rainfall is an exogenous variable whose value will be determined by other factors like humidity, air speed etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cSitojkVlFE-"
      },
      "source": [
        "import statsmodels.api as sm"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppqfJBw1lFE-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef4d0eb4-0674-44ba-a0f8-7cc17c4a9841"
      },
      "source": [
        "model=sm.tsa.statespace.SARIMAX(df['Sales'],order=(1, 1, 1),seasonal_order=(1,1,1,12))\n",
        "results=model.fit()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
            "  % freq, ValueWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "G3Y74_9vSXfc",
        "outputId": "61e2259d-888c-4d07-ce3f-def6a624318f"
      },
      "source": [
        "results.summary()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Statespace Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>                <td>Sales</td>             <th>  No. Observations:  </th>    <td>105</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)x(1, 1, 1, 12)</td> <th>  Log Likelihood     </th> <td>-738.402</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Mon, 23 Nov 2020</td>        <th>  AIC                </th> <td>1486.804</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>04:11:26</td>            <th>  BIC                </th> <td>1499.413</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                    <td>01-01-1964</td>           <th>  HQIC               </th> <td>1491.893</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                          <td>- 09-01-1972</td>          <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>    <td>    0.2790</td> <td>    0.081</td> <td>    3.433</td> <td> 0.001</td> <td>    0.120</td> <td>    0.438</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>    <td>   -0.9494</td> <td>    0.043</td> <td>  -22.334</td> <td> 0.000</td> <td>   -1.033</td> <td>   -0.866</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.S.L12</th> <td>   -0.4544</td> <td>    0.303</td> <td>   -1.499</td> <td> 0.134</td> <td>   -1.049</td> <td>    0.140</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.S.L12</th> <td>    0.2450</td> <td>    0.311</td> <td>    0.788</td> <td> 0.431</td> <td>   -0.365</td> <td>    0.855</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>   <td> 5.055e+05</td> <td> 6.12e+04</td> <td>    8.265</td> <td> 0.000</td> <td> 3.86e+05</td> <td> 6.25e+05</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (Q):</th>          <td>40.24</td> <th>  Jarque-Bera (JB):  </th> <td>8.70</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.46</td>  <th>  Prob(JB):          </th> <td>0.01</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>1.18</td>  <th>  Skew:              </th> <td>-0.21</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.64</td>  <th>  Kurtosis:          </th> <td>4.45</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                 Statespace Model Results                                 \n",
              "==========================================================================================\n",
              "Dep. Variable:                              Sales   No. Observations:                  105\n",
              "Model:             SARIMAX(1, 1, 1)x(1, 1, 1, 12)   Log Likelihood                -738.402\n",
              "Date:                            Mon, 23 Nov 2020   AIC                           1486.804\n",
              "Time:                                    04:11:26   BIC                           1499.413\n",
              "Sample:                                01-01-1964   HQIC                          1491.893\n",
              "                                     - 09-01-1972                                         \n",
              "Covariance Type:                              opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1          0.2790      0.081      3.433      0.001       0.120       0.438\n",
              "ma.L1         -0.9494      0.043    -22.334      0.000      -1.033      -0.866\n",
              "ar.S.L12      -0.4544      0.303     -1.499      0.134      -1.049       0.140\n",
              "ma.S.L12       0.2450      0.311      0.788      0.431      -0.365       0.855\n",
              "sigma2      5.055e+05   6.12e+04      8.265      0.000    3.86e+05    6.25e+05\n",
              "===================================================================================\n",
              "Ljung-Box (Q):                       40.24   Jarque-Bera (JB):                 8.70\n",
              "Prob(Q):                              0.46   Prob(JB):                         0.01\n",
              "Heteroskedasticity (H):               1.18   Skew:                            -0.21\n",
              "Prob(H) (two-sided):                  0.64   Kurtosis:                         4.45\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzEIFlgtTjxs"
      },
      "source": [
        "#Model Diagnostic plot to check the residuals"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "VuGUIy3vTpLU",
        "outputId": "1697596b-7a0e-4123-8028-16b9764cbe4b"
      },
      "source": [
        "results.plot_diagnostics(figsize=(10, 8))\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dup2VWF6TfIk"
      },
      "source": [
        "#SARIMAX Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rmAlVSXSlFE_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "09b80141-fe8c-4d7d-d6d9-0841cbc8400b"
      },
      "source": [
        "df['forecast']=results.predict(start=90,end=103,dynamic=True)\n",
        "df[['Sales','forecast']].plot(figsize=(12,8))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa49584e828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ps2GQUrUlFE_"
      },
      "source": [
        "from pandas.tseries.offsets import DateOffset\n",
        "future_dates=[df.index[-1]+ DateOffset(months=x)for x in range(0,24)]"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChzwUfQplFE_"
      },
      "source": [
        "future_datest_df=pd.DataFrame(index=future_dates[1:],columns=df.columns)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3l1bbkPxlFE_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f29105f3-7879-4560-f338-cf79cd0f1fd5"
      },
      "source": [
        "future_datest_df.tail()"
      ],
      "execution_count": 48,
      "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>Sales</th>\n",
              "      <th>Sales First Difference</th>\n",
              "      <th>Seasonal First Difference</th>\n",
              "      <th>forecast</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1974-04-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-05-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-06-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-07-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-08-01</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Sales Sales First Difference Seasonal First Difference forecast\n",
              "1974-04-01   NaN                    NaN                       NaN      NaN\n",
              "1974-05-01   NaN                    NaN                       NaN      NaN\n",
              "1974-06-01   NaN                    NaN                       NaN      NaN\n",
              "1974-07-01   NaN                    NaN                       NaN      NaN\n",
              "1974-08-01   NaN                    NaN                       NaN      NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0XXKJUdflFE_"
      },
      "source": [
        "future_df=pd.concat([df,future_datest_df])"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2ZavgCrlFE_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "edff58ab-b23e-4ce2-c523-f4d12597fbdb"
      },
      "source": [
        "future_df['forecast'] = results.predict(start = 104, end = 120, dynamic= True)  \n",
        "future_df[['Sales', 'forecast']].plot(figsize=(12, 8)) "
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa49579a8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}