Forecasting_Airlines_Passenger_Data.ipynb

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        "Forecast the Airlines Passengers data set. \n",
        "\n",
        "Prepare a document for each model explaining how many dummy variables you have created and MAE value for each model. \n",
        "\n",
        "Finally which model you will use for \n",
        "Forecasting.\n"
      ],
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      "source": [
        "**Load libraries and data**"
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      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sms\n",
        "import pandas as pd\n",
        "\n",
        "%matplotlib inline"
      ]
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              "       Month  Passengers\n",
              "0 1995-01-01         112\n",
              "1 1995-02-01         118\n",
              "2 1995-03-01         132\n",
              "3 1995-04-01         129\n",
              "4 1995-05-01         121"
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        "df_airline=pd.read_excel('/content/Airlines+Data.xlsx')\n",
        "df_airline.head()"
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              "        Month  Passengers\n",
              "91 2002-08-01         405\n",
              "92 2002-09-01         355\n",
              "93 2002-10-01         306\n",
              "94 2002-11-01         271\n",
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        "df_airline.tail()"
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        "**EDA**"
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              "Month         0\n",
              "Passengers    0\n",
              "dtype: int64"
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        "df_airline.isnull().sum()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 96 entries, 0 to 95\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   Month       96 non-null     datetime64[ns]\n",
            " 1   Passengers  96 non-null     int64         \n",
            "dtypes: datetime64[ns](1), int64(1)\n",
            "memory usage: 1.6 KB\n"
          ]
        }
      ],
      "source": [
        "df_airline.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 223,
      "id": "1b5d6bb1",
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      "source": [
        "#Get Month column as index of data\n",
        "df_airline.set_index('Month',inplace=True)"
      ]
    },
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              "            Passengers\n",
              "Month                 \n",
              "1995-01-01         112\n",
              "1995-02-01         118\n",
              "1995-03-01         132\n",
              "1995-04-01         129\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d4bdc04c-d936-4979-b0fb-a9b31e65157d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d4bdc04c-d936-4979-b0fb-a9b31e65157d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 224
        }
      ],
      "source": [
        "df_airline.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualisations**"
      ],
      "metadata": {
        "id": "gNwhpsYhMfwd"
      },
      "id": "gNwhpsYhMfwd"
    },
    {
      "cell_type": "code",
      "execution_count": 225,
      "id": "fd83f7a1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "fd83f7a1",
        "outputId": "23a25c6c-238e-4656-c8d4-6965449a04ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f34c444b490>"
            ]
          },
          "metadata": {},
          "execution_count": 225
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_airline.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- There is an upward trending data and there is seasonality in our data."
      ],
      "metadata": {
        "id": "mwt-I0mHO6rK"
      },
      "id": "mwt-I0mHO6rK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Histogram"
      ],
      "metadata": {
        "id": "oBGE9F_ANGEM"
      },
      "id": "oBGE9F_ANGEM"
    },
    {
      "cell_type": "code",
      "source": [
        "df_airline.hist()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "a39H51gQMrIa",
        "outputId": "1280d2a9-199e-4e50-c70e-6334cb9250b1"
      },
      "id": "a39H51gQMrIa",
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Distplot"
      ],
      "metadata": {
        "id": "NHJ8265oNC6p"
      },
      "id": "NHJ8265oNC6p"
    },
    {
      "cell_type": "code",
      "source": [
        "df_airline.plot(kind='kde')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "nwp9kJ5SM7nn",
        "outputId": "b0bfe21e-11cb-43cb-f2c6-f0bb0bdedc4d"
      },
      "id": "nwp9kJ5SM7nn",
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We can observe this a normally distributed data."
      ],
      "metadata": {
        "id": "5VdJM1S8OxsR"
      },
      "id": "5VdJM1S8OxsR"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lagplot"
      ],
      "metadata": {
        "id": "Wza_dGVKNbEz"
      },
      "id": "Wza_dGVKNbEz"
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.plotting import lag_plot\n",
        "lag_plot(df_airline)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "WJFE4YFhNRY3",
        "outputId": "f589611c-b530-4d68-abcf-04d3b6d3c638"
      },
      "id": "WJFE4YFhNRY3",
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f34c42b5090>"
            ]
          },
          "metadata": {},
          "execution_count": 228
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
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    {
      "cell_type": "code",
      "source": [
        "airline=pd.read_excel('/content/Airlines+Data.xlsx')\n",
        "airline.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "mufCsAl0N1Mg",
        "outputId": "5d213967-16ff-4489-b5c9-68217e7bf404"
      },
      "id": "mufCsAl0N1Mg",
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Month  Passengers\n",
              "0 1995-01-01         112\n",
              "1 1995-02-01         118\n",
              "2 1995-03-01         132\n",
              "3 1995-04-01         129\n",
              "4 1995-05-01         121"
            ],
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              "      <th></th>\n",
              "      <th>Month</th>\n",
              "      <th>Passengers</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1995-01-01</td>\n",
              "      <td>112</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1995-02-01</td>\n",
              "      <td>118</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1995-03-01</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995-04-01</td>\n",
              "      <td>129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1995-05-01</td>\n",
              "      <td>121</td>\n",
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              "\n",
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              "          document.querySelector('#df-df892c7c-3bda-4796-bbd0-c8269dcbe957 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-df892c7c-3bda-4796-bbd0-c8269dcbe957');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 229
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get month and year columns in our data\n",
        "\n",
        "airline[\"month\"] = airline.Month.dt.strftime(\"%b\") # month extraction\n",
        "airline[\"year\"] = airline.Month.dt.strftime(\"%Y\") # year extraction\n",
        "airline.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0vymI8PGNraA",
        "outputId": "d4e0f9b1-a51c-41eb-9e63-78360c89fddd"
      },
      "id": "0vymI8PGNraA",
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Month  Passengers month  year\n",
              "0 1995-01-01         112   Jan  1995\n",
              "1 1995-02-01         118   Feb  1995\n",
              "2 1995-03-01         132   Mar  1995\n",
              "3 1995-04-01         129   Apr  1995\n",
              "4 1995-05-01         121   May  1995"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <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>Month</th>\n",
              "      <th>Passengers</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1995-01-01</td>\n",
              "      <td>112</td>\n",
              "      <td>Jan</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1995-02-01</td>\n",
              "      <td>118</td>\n",
              "      <td>Feb</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1995-03-01</td>\n",
              "      <td>132</td>\n",
              "      <td>Mar</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995-04-01</td>\n",
              "      <td>129</td>\n",
              "      <td>Apr</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1995-05-01</td>\n",
              "      <td>121</td>\n",
              "      <td>May</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d10b7c3-10c5-430f-85f7-37ef3bf864c0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8d10b7c3-10c5-430f-85f7-37ef3bf864c0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8d10b7c3-10c5-430f-85f7-37ef3bf864c0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Heatmap"
      ],
      "metadata": {
        "id": "zQreGDOGOr44"
      },
      "id": "zQreGDOGOr44"
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(12,8))\n",
        "heatmap_y_month = pd.pivot_table(data=airline,values=\"Passengers\",index=\"year\",columns=\"month\",aggfunc=\"mean\",fill_value=0)\n",
        "sns.heatmap(heatmap_y_month,annot=True,fmt=\"g\") #fmt is format of the grid values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "3wzlBZbSOIeF",
        "outputId": "87473922-e6c2-46e5-a77c-1c54490da02e"
      },
      "id": "3wzlBZbSOIeF",
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f34c4212710>"
            ]
          },
          "metadata": {},
          "execution_count": 231
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Years 2000 and after have higher number of passengers.\n",
        "- Months Jul and Aug fairly high passengers and months Nov and Dec have lower passengers tarvelling. "
      ],
      "metadata": {
        "id": "110Lg2ALPTaU"
      },
      "id": "110Lg2ALPTaU"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Boxplot"
      ],
      "metadata": {
        "id": "dOESubIIOqC3"
      },
      "id": "dOESubIIOqC3"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.subplot(211)\n",
        "sns.boxplot(x=\"month\",y=\"Passengers\",data=airline)\n",
        "plt.subplot(212)\n",
        "sns.boxplot(x=\"year\",y=\"Passengers\",data=airline)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "bVGygaAYOfx9",
        "outputId": "c239e223-4797-4789-92f2-7df34966355a"
      },
      "id": "bVGygaAYOfx9",
      "execution_count": 232,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f34c42ee450>"
            ]
          },
          "metadata": {},
          "execution_count": 232
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Similar observations can be concluded from boxplot as well."
      ],
      "metadata": {
        "id": "0ynwgCGaPy5C"
      },
      "id": "0ynwgCGaPy5C"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,3))\n",
        "sns.lineplot(x=\"year\",y=\"Passengers\",data=airline)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "A2_FieYEQBVV",
        "outputId": "e8ac8cd9-a9a8-4854-905f-88c8b71c4191"
      },
      "id": "A2_FieYEQBVV",
      "execution_count": 233,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f34c41ae9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 233
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x216 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Moving Average"
      ],
      "metadata": {
        "id": "0g7-KwhJi9Cm"
      },
      "id": "0g7-KwhJi9Cm"
    },
    {
      "cell_type": "code",
      "source": [
        "#Smoothning of the graph\n",
        "plt.figure(figsize=(12,4))\n",
        "airline.Passengers.plot(label=\"org\")\n",
        "for i in range(2,30,8):\n",
        "    airline[\"Passengers\"].rolling(i).mean().plot(label=str(i))\n",
        "plt.legend(loc='best')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ZJCNPvDMi-J-",
        "outputId": "685da976-4c71-4a4b-a4c3-4bf67c3154a1"
      },
      "execution_count": 234,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f34c522e1d0>"
            ]
          },
          "metadata": {},
          "execution_count": 234
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "ZJCNPvDMi-J-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Time series decomposition plot"
      ],
      "metadata": {
        "id": "ROW2VaKZjX60"
      },
      "id": "ROW2VaKZjX60"
    },
    {
      "cell_type": "code",
      "source": [
        "decompose_ts_add = seasonal_decompose(airline.Passengers,period=12)\n",
        "decompose_ts_add.plot()\n",
        "plt.show()                                           #1st is the original plot, 2nd in the trend, 3rd is the seaonality component, 4th is the noise component"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "hiKiw4dTjKwv",
        "outputId": "49d54a34-3827-4e36-ba06-df80b5ab57ff"
      },
      "execution_count": 235,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "hiKiw4dTjKwv"
    },
    {
      "cell_type": "markdown",
      "source": [
        "ACF Plot"
      ],
      "metadata": {
        "id": "JCpoLj6mSJG1"
      },
      "id": "JCpoLj6mSJG1"
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf\n",
        "acf = plot_acf(airline[\"Passengers\"])                                                   #q=6"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "hBXHyN92RTc-",
        "outputId": "2fafbd0c-057b-424c-cd7a-e3858b4eada1"
      },
      "id": "hBXHyN92RTc-",
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "PACF Plot"
      ],
      "metadata": {
        "id": "fm1wfgzESM9e"
      },
      "id": "fm1wfgzESM9e"
    },
    {
      "cell_type": "code",
      "source": [
        "pacf = plot_pacf(airline[\"Passengers\"])                               #p=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "qG54DM14Rj-k",
        "outputId": "bdb9747a-611b-4b2a-b111-188501a92f34"
      },
      "id": "qG54DM14Rj-k",
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Forecasting Model Based Methods**"
      ],
      "metadata": {
        "id": "Lpyft1uMiXpH"
      },
      "id": "Lpyft1uMiXpH"
    },
    {
      "cell_type": "markdown",
      "source": [
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              "(96, 4)"
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        "id": "I28ctje6S0OF",
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      "id": "I28ctje6S0OF",
      "execution_count": 239,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "        Month  Passengers month  year\n",
              "0  1995-01-01         112   Jan  1995\n",
              "1  1995-02-01         118   Feb  1995\n",
              "2  1995-03-01         132   Mar  1995\n",
              "3  1995-04-01         129   Apr  1995\n",
              "4  1995-05-01         121   May  1995\n",
              "..        ...         ...   ...   ...\n",
              "91 2002-08-01         405   Aug  2002\n",
              "92 2002-09-01         355   Sep  2002\n",
              "93 2002-10-01         306   Oct  2002\n",
              "94 2002-11-01         271   Nov  2002\n",
              "95 2002-12-01         306   Dec  2002\n",
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              "[96 rows x 4 columns]"
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      "cell_type": "code",
      "source": [
        "airline.info()"
      ],
      "metadata": {
        "colab": {
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        "id": "xdb31v4QUuzb",
        "outputId": "b49799ab-36a1-4969-c6d1-d78fe04df1a4"
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      "id": "xdb31v4QUuzb",
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 96 entries, 0 to 95\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   Month       96 non-null     datetime64[ns]\n",
            " 1   Passengers  96 non-null     int64         \n",
            " 2   month       96 non-null     object        \n",
            " 3   year        96 non-null     object        \n",
            "dtypes: datetime64[ns](1), int64(1), object(2)\n",
            "memory usage: 3.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert dtype of year \n",
        "airline['year'] = airline['year'].astype(\"int64\")"
      ],
      "metadata": {
        "id": "EqRdKdTKUz2k"
      },
      "id": "EqRdKdTKUz2k",
      "execution_count": 241,
      "outputs": []
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      "id": "wOGH47AcVYEc",
      "execution_count": 242,
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        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 96 entries, 0 to 95\n",
            "Data columns (total 4 columns):\n",
            " #   Column      Non-Null Count  Dtype         \n",
            "---  ------      --------------  -----         \n",
            " 0   Month       96 non-null     datetime64[ns]\n",
            " 1   Passengers  96 non-null     int64         \n",
            " 2   month       96 non-null     object        \n",
            " 3   year        96 non-null     int64         \n",
            "dtypes: datetime64[ns](1), int64(2), object(1)\n",
            "memory usage: 3.1+ KB\n"
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    {
      "cell_type": "markdown",
      "source": [
        "**Creating dummy variables**"
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      "metadata": {
        "id": "lEOmuyXKCmP9"
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      "id": "lEOmuyXKCmP9"
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    {
      "cell_type": "code",
      "source": [
        "#OHE for month column\n",
        "airline1 = pd.get_dummies(airline)\n",
        "airline1"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
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        "id": "fPgmWeQ1TuOb",
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      "id": "fPgmWeQ1TuOb",
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "execute_result",
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              "        Month  Passengers  year  month_Apr  month_Aug  month_Dec  month_Feb  \\\n",
              "0  1995-01-01         112  1995          0          0          0          0   \n",
              "1  1995-02-01         118  1995          0          0          0          1   \n",
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              "\n",
              "    month_Jan  month_Jul  month_Jun  month_Mar  month_May  month_Nov  \\\n",
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              "91          0          0          0          0          0          0   \n",
              "92          0          0          0          0          0          0   \n",
              "93          0          0          0          0          0          0   \n",
              "94          0          0          0          0          0          1   \n",
              "95          0          0          0          0          0          0   \n",
              "\n",
              "    month_Oct  month_Sep  \n",
              "0           0          0  \n",
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              "2           0          0  \n",
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              "91          0          0  \n",
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              "      <td>355</td>\n",
              "      <td>2002</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>2002-10-01</td>\n",
              "      <td>306</td>\n",
              "      <td>2002</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>2002-11-01</td>\n",
              "      <td>271</td>\n",
              "      <td>2002</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>2002-12-01</td>\n",
              "      <td>306</td>\n",
              "      <td>2002</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>96 rows × 15 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9352069-4a94-41f7-8c4f-de7722bf161b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b9352069-4a94-41f7-8c4f-de7722bf161b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b9352069-4a94-41f7-8c4f-de7722bf161b');\n",
              "          const dataTable =\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 243
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.arange(1,97)"
      ],
      "metadata": {
        "id": "3SyCopHU_KB_"
      },
      "id": "3SyCopHU_KB_",
      "execution_count": 244,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#timesteps\n",
        "t=pd.Series(data=x)\n",
        "t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j20mLjQI_b0E",
        "outputId": "c07ce8da-2337-4fea-a355-6e925f26c396"
      },
      "id": "j20mLjQI_b0E",
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      2\n",
              "2      3\n",
              "3      4\n",
              "4      5\n",
              "      ..\n",
              "91    92\n",
              "92    93\n",
              "93    94\n",
              "94    95\n",
              "95    96\n",
              "Length: 96, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#timesteps square values\n",
        "t_sqr=pd.Series(data=(x*x))\n",
        "t_sqr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRz6VMYJASK0",
        "outputId": "9f0662db-c65f-4c8a-ee7b-d573cd90f258"
      },
      "id": "vRz6VMYJASK0",
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1\n",
              "1        4\n",
              "2        9\n",
              "3       16\n",
              "4       25\n",
              "      ... \n",
              "91    8464\n",
              "92    8649\n",
              "93    8836\n",
              "94    9025\n",
              "95    9216\n",
              "Length: 96, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#log of number of passengers\n",
        "airline1['log_passengers'] = np.log(airline1['Passengers'])"
      ],
      "metadata": {
        "id": "oPDL9YtTC2oN"
      },
      "id": "oPDL9YtTC2oN",
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "airline1['t'] = t"
      ],
      "metadata": {
        "id": "aM7oHiCxWkqY"
      },
      "id": "aM7oHiCxWkqY",
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "airline1['t_sqr'] = t_sqr"
      ],
      "metadata": {
        "id": "A2w5xFvBAZja"
      },
      "id": "A2w5xFvBAZja",
      "execution_count": 249,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "airline1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "Xmh7U0eH_XBX",
        "outputId": "21f5937c-303f-4bae-cbbb-86502a3fa6e2"
      },
      "id": "Xmh7U0eH_XBX",
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Month  Passengers  year  month_Apr  month_Aug  month_Dec  month_Feb  \\\n",
              "0 1995-01-01         112  1995          0          0          0          0   \n",
              "1 1995-02-01         118  1995          0          0          0          1   \n",
              "2 1995-03-01         132  1995          0          0          0          0   \n",
              "3 1995-04-01         129  1995          1          0          0          0   \n",
              "4 1995-05-01         121  1995          0          0          0          0   \n",
              "\n",
              "   month_Jan  month_Jul  month_Jun  month_Mar  month_May  month_Nov  \\\n",
              "0          1          0          0          0          0          0   \n",
              "1          0          0          0          0          0          0   \n",
              "2          0          0          0          1          0          0   \n",
              "3          0          0          0          0          0          0   \n",
              "4          0          0          0          0          1          0   \n",
              "\n",
              "   month_Oct  month_Sep  log_passengers  t  t_sqr  \n",
              "0          0          0        4.718499  1      1  \n",
              "1          0          0        4.770685  2      4  \n",
              "2          0          0        4.882802  3      9  \n",
              "3          0          0        4.859812  4     16  \n",
              "4          0          0        4.795791  5     25  "
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              "      <th>year</th>\n",
              "      <th>month_Apr</th>\n",
              "      <th>month_Aug</th>\n",
              "      <th>month_Dec</th>\n",
              "      <th>month_Feb</th>\n",
              "      <th>month_Jan</th>\n",
              "      <th>month_Jul</th>\n",
              "      <th>month_Jun</th>\n",
              "      <th>month_Mar</th>\n",
              "      <th>month_May</th>\n",
              "      <th>month_Nov</th>\n",
              "      <th>month_Oct</th>\n",
              "      <th>month_Sep</th>\n",
              "      <th>log_passengers</th>\n",
              "      <th>t</th>\n",
              "      <th>t_sqr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1995-01-01</td>\n",
              "      <td>112</td>\n",
              "      <td>1995</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1995-02-01</td>\n",
              "      <td>118</td>\n",
              "      <td>1995</td>\n",
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              "      <td>4.770685</td>\n",
              "      <td>2</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1995-03-01</td>\n",
              "      <td>132</td>\n",
              "      <td>1995</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>4.882802</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1995-04-01</td>\n",
              "      <td>129</td>\n",
              "      <td>1995</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.859812</td>\n",
              "      <td>4</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1995-05-01</td>\n",
              "      <td>121</td>\n",
              "      <td>1995</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.795791</td>\n",
              "      <td>5</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a469b35-184a-4234-a017-09d606319aff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2a469b35-184a-4234-a017-09d606319aff button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a469b35-184a-4234-a017-09d606319aff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Split Train and Test data**"
      ],
      "metadata": {
        "id": "8zY6EoSDSj9P"
      },
      "id": "8zY6EoSDSj9P"
    },
    {
      "cell_type": "code",
      "source": [
        "Train = airline1.head(84)\n",
        "Test = airline1.tail(12)"
      ],
      "metadata": {
        "id": "WngOVCnFSn2u"
      },
      "id": "WngOVCnFSn2u",
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model Building**"
      ],
      "metadata": {
        "id": "LmxPvkqtaxdn"
      },
      "id": "LmxPvkqtaxdn"
    },
    {
      "cell_type": "code",
      "source": [
        "def MAE(pred,org):\n",
        "    temp = np.abs((pred-org)/org)\n",
        "    return np.mean(temp)"
      ],
      "metadata": {
        "id": "y7rKSgr0nyum"
      },
      "id": "y7rKSgr0nyum",
      "execution_count": 252,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Linear Model\n",
        "import statsmodels.formula.api as smf \n",
        "\n",
        "linear_model = smf.ols('Passengers~t',data=Train).fit()                                                              #Fit the train model\n",
        "pred_linear =  pd.Series(linear_model.predict(pd.DataFrame(Test['t'])))                                              #predict on test data   \n",
        "mae_linear = MAE(Test['Passengers'],pred_linear)                                                                     #calculate eoot mean sqr error \n",
        "mae_linear"
      ],
      "metadata": {
        "id": "nmlL0HjxWRoe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db08511c-d7f9-41ba-cc53-aeaa33a67f9c"
      },
      "id": "nmlL0HjxWRoe",
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.12875898544471487"
            ]
          },
          "metadata": {},
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exponential\n",
        "\n",
        "Exp = smf.ols('log_passengers~t',data=Train).fit()\n",
        "pred_Exp = pd.Series(Exp.predict(pd.DataFrame(Test['t'])))\n",
        "mae_Exp = MAE(Test['Passengers'], np.exp(pred_Exp))\n",
        "mae_Exp"
      ],
      "metadata": {
        "id": "wiVntFHjVu2a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b399a0f4-cb09-4f93-f80e-7052f614aa6b"
      },
      "id": "wiVntFHjVu2a",
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11102459391986047"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Quadratic \n",
        "\n",
        "Quad = smf.ols('Passengers~t+t_sqr',data=Train).fit()\n",
        "pred_Quad = pd.Series(Quad.predict(Test[[\"t\",\"t_sqr\"]]))\n",
        "mae_Quad = MAE(Test['Passengers'],pred_Quad)\n",
        "mae_Quad"
      ],
      "metadata": {
        "id": "b9CG1Al6Vuwi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "267388e7-9075-41c9-8d21-e30859d8114f"
      },
      "id": "b9CG1Al6Vuwi",
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.11838168080316025"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Additive seasonality \n",
        "\n",
        "add_sea = smf.ols('Passengers~month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data=Train).fit()\n",
        "pred_add_sea = pd.Series(add_sea.predict(Test[['month_Jan','month_Feb','month_Mar','month_Apr','month_May','month_Jun','month_Jul','month_Aug','month_Sep','month_Oct','month_Nov']]))\n",
        "mae_add_sea = MAE(Test['Passengers'],pred_add_sea)\n",
        "mae_add_sea"
      ],
      "metadata": {
        "id": "zppSLBOKVuqw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d617cfde-64b5-4c98-e992-497a07b4f4e8"
      },
      "id": "zppSLBOKVuqw",
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6601957591766386"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Additive Seasonality Quadratic \n",
        "\n",
        "add_sea_Quad = smf.ols('Passengers~t+t_sqr+month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data=Train).fit()\n",
        "pred_add_sea_quad = pd.Series(add_sea_Quad.predict(Test[['month_Jan','month_Feb','month_Mar','month_Apr','month_May','month_Jun','month_Jul','month_Aug','month_Sep','month_Oct','month_Nov','t','t_sqr']]))\n",
        "mae_add_sea_quad = MAE(Test['Passengers'], pred_add_sea_quad)\n",
        "mae_add_sea_quad"
      ],
      "metadata": {
        "id": "crbp1lYVVukV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c8f3165-11f7-49dc-89e1-5ef10206db97"
      },
      "id": "crbp1lYVVukV",
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.055710861779900894"
            ]
          },
          "metadata": {},
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Multiplicative Seasonality\n",
        "\n",
        "Mul_sea = smf.ols('log_passengers~month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data = Train).fit()\n",
        "pred_Mult_sea = pd.Series(Mul_sea.predict(Test))\n",
        "mae_Mult_sea = MAE(Test['Passengers'], np.exp(pred_Mult_sea))\n",
        "mae_Mult_sea"
      ],
      "metadata": {
        "id": "zXX6xeSnVueo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3702a50b-5c9d-4169-de32-d4fe964470e3"
      },
      "id": "zXX6xeSnVueo",
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7218240575033881"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Multiplicative Additive Seasonality \n",
        "\n",
        "Mul_Add_sea = smf.ols('log_passengers~t+month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data = Train).fit()\n",
        "pred_Mult_add_sea = pd.Series(Mul_Add_sea.predict(Test))\n",
        "mae_Mult_add_sea = MAE(Test['Passengers'], np.exp(pred_Mult_add_sea))\n",
        "mae_Mult_add_sea "
      ],
      "metadata": {
        "id": "PPO-SZ9pVuX3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fe2db2d-aba1-43f6-b2cd-6e2c291b70b1"
      },
      "id": "PPO-SZ9pVuX3",
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.024187475026051352"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Forecasing by Data driven Model**"
      ],
      "metadata": {
        "id": "LLxtTJAqiSG3"
      },
      "id": "LLxtTJAqiSG3"
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose\n",
        "from statsmodels.tsa.holtwinters import SimpleExpSmoothing # SES\n",
        "from statsmodels.tsa.holtwinters import Holt # Holts Exponential Smoothing\n",
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing"
      ],
      "metadata": {
        "id": "SaeIookriR2T"
      },
      "id": "SaeIookriR2T",
      "execution_count": 260,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "airline.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tCRxqni1oJZH",
        "outputId": "64961544-acfe-46dd-d1de-3600208c4a1c"
      },
      "id": "tCRxqni1oJZH",
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Month  Passengers month  year\n",
              "0 1995-01-01         112   Jan  1995\n",
              "1 1995-02-01         118   Feb  1995\n",
              "2 1995-03-01         132   Mar  1995\n",
              "3 1995-04-01         129   Apr  1995\n",
              "4 1995-05-01         121   May  1995"
            ],
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Train = airline.head(84)\n",
        "Test = airline.tail(12)"
      ],
      "metadata": {
        "id": "DEGnlnP0iy-F"
      },
      "id": "DEGnlnP0iy-F",
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Simple Exponential Method**"
      ],
      "metadata": {
        "id": "-md1B8OJkj1V"
      },
      "id": "-md1B8OJkj1V"
    },
    {
      "cell_type": "code",
      "source": [
        "ses_model = SimpleExpSmoothing(Train[\"Passengers\"]).fit(smoothing_level=0.2)\n",
        "pred_ses = ses_model.predict(start = Test.index[0],end = Test.index[-1])\n",
        "MAE_ses = MAE(pred_ses,Test.Passengers) \n",
        "MAE_ses"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nf8AZcTvkhsT",
        "outputId": "d8251979-0c48-43ef-b4cc-ff80cc164db1"
      },
      "id": "Nf8AZcTvkhsT",
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1341127474051382"
            ]
          },
          "metadata": {},
          "execution_count": 263
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Holt method**"
      ],
      "metadata": {
        "id": "aHqgQIF4kzQH"
      },
      "id": "aHqgQIF4kzQH"
    },
    {
      "cell_type": "code",
      "source": [
        "# Holt method \n",
        "hw_model = Holt(Train[\"Passengers\"]).fit(smoothing_level=0.8, smoothing_slope=0.2)\n",
        "pred_hw = hw_model.predict(start = Test.index[0],end = Test.index[-1])\n",
        "MAE_hw = MAE(pred_hw,Test.Passengers) \n",
        "MAE_hw"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwnLBbKhkvfg",
        "outputId": "bc227504-52ee-4b91-c972-bf52df38a3b6"
      },
      "id": "QwnLBbKhkvfg",
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.23530719652100632"
            ]
          },
          "metadata": {},
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Holts winter exponential smoothing with additive seasonality and additive trend**"
      ],
      "metadata": {
        "id": "3cWOK_5qlC10"
      },
      "id": "3cWOK_5qlC10"
    },
    {
      "cell_type": "code",
      "source": [
        "hwe_model_add_add = ExponentialSmoothing(Train[\"Passengers\"],seasonal=\"add\",trend=\"add\",seasonal_periods=12).fit() #add the trend to the model\n",
        "pred_hwe_add_add = hwe_model_add_add.predict(start = Test.index[0],end = Test.index[-1])\n",
        "MAE__hwe_add_add = MAE(pred_hwe_add_add,Test.Passengers) \n",
        "MAE__hwe_add_add"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KD-4WueylHMd",
        "outputId": "47185325-a9f5-450f-eb0f-e6c18e6eaa21"
      },
      "id": "KD-4WueylHMd",
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05499858437809377"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Holts winter exponential smoothing with multiplicative seasonality and additive trend**"
      ],
      "metadata": {
        "id": "uVAmtwGOlVrj"
      },
      "id": "uVAmtwGOlVrj"
    },
    {
      "cell_type": "code",
      "source": [
        "hwe_model_mul_add = ExponentialSmoothing(Train[\"Passengers\"],seasonal=\"mul\",trend=\"add\",seasonal_periods=12).fit() \n",
        "pred_hwe_mul_add = hwe_model_mul_add.predict(start = Test.index[0],end = Test.index[-1])\n",
        "MAE__hwe_mul_add = MAE(pred_hwe_mul_add,Test.Passengers)\n",
        "MAE__hwe_mul_add"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMWsM12zlXDd",
        "outputId": "9c359666-9ed3-4c5d-9189-ce611f5ceccc"
      },
      "id": "zMWsM12zlXDd",
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.028211184081344768"
            ]
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Forecasting by ARIMA Model**"
      ],
      "metadata": {
        "id": "lA8fBd2NWLAB"
      },
      "id": "lA8fBd2NWLAB"
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import sqrt\n",
        "import warnings\n",
        "import itertools\n",
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "lbyZp03vWKnH"
      },
      "id": "lbyZp03vWKnH",
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_airline.head()"
      ],
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      },
      "id": "4pht_wJJWa4B",
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Passengers\n",
              "Month                 \n",
              "1995-01-01         112\n",
              "1995-02-01         118\n",
              "1995-03-01         132\n",
              "1995-04-01         129\n",
              "1995-05-01         121"
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separate out a validation dataset\n",
        "split_point = len(df_airline) - 12\n",
        "dataset, validation = df_airline[0:split_point], df_airline[split_point:]\n",
        "print('Dataset %d, Validation %d' % (len(dataset), len(validation)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GRX1k_FvWax3",
        "outputId": "3494a39a-c4a9-4329-8a4d-1f4a284c3005"
      },
      "id": "GRX1k_FvWax3",
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset 84, Validation 12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Persistence/ Base model**"
      ],
      "metadata": {
        "id": "LfRc3whTXAu1"
      },
      "id": "LfRc3whTXAu1"
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate a persistence model\n",
        "\n",
        "\n",
        "# prepare data\n",
        "X = dataset.values\n",
        "X = X.astype('float32')\n",
        "train_size = int(len(X) * 0.50)\n",
        "train, test = X[0:train_size], X[train_size:]"
      ],
      "metadata": {
        "id": "EJnuu7RJYThl"
      },
      "id": "EJnuu7RJYThl",
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.size, test.size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lr9HQnHKeKbK",
        "outputId": "37e24e7f-5b60-4155-e9ab-818a35c351c7"
      },
      "id": "Lr9HQnHKeKbK",
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42, 42)"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "# walk-forward validation\n",
        "history = [x for x in train]\n",
        "predictions = list()\n",
        "for i in range(len(test)):\n",
        "    yhat = history[-1]\n",
        "    predictions.append(yhat)\n",
        "    obs = test[i]\n",
        "    history.append(obs)\n",
        "    print('>Predicted=%.3f, Expected=%.3f' % (yhat, obs))\n",
        "# report performance\n",
        "MAE_base = MAE(predictions, test)\n",
        "print('MAE_base: %.3f' % MAE_base)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KbZl3Z09Wasa",
        "outputId": "2a3e661f-9865-4cc6-b7b4-67e2df04be06"
      },
      "id": "KbZl3Z09Wasa",
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">Predicted=218.000, Expected=230.000\n",
            ">Predicted=230.000, Expected=242.000\n",
            ">Predicted=242.000, Expected=209.000\n",
            ">Predicted=209.000, Expected=191.000\n",
            ">Predicted=191.000, Expected=172.000\n",
            ">Predicted=172.000, Expected=194.000\n",
            ">Predicted=194.000, Expected=196.000\n",
            ">Predicted=196.000, Expected=196.000\n",
            ">Predicted=196.000, Expected=236.000\n",
            ">Predicted=236.000, Expected=235.000\n",
            ">Predicted=235.000, Expected=229.000\n",
            ">Predicted=229.000, Expected=243.000\n",
            ">Predicted=243.000, Expected=264.000\n",
            ">Predicted=264.000, Expected=272.000\n",
            ">Predicted=272.000, Expected=237.000\n",
            ">Predicted=237.000, Expected=211.000\n",
            ">Predicted=211.000, Expected=180.000\n",
            ">Predicted=180.000, Expected=201.000\n",
            ">Predicted=201.000, Expected=204.000\n",
            ">Predicted=204.000, Expected=188.000\n",
            ">Predicted=188.000, Expected=235.000\n",
            ">Predicted=235.000, Expected=227.000\n",
            ">Predicted=227.000, Expected=234.000\n",
            ">Predicted=234.000, Expected=264.000\n",
            ">Predicted=264.000, Expected=302.000\n",
            ">Predicted=302.000, Expected=293.000\n",
            ">Predicted=293.000, Expected=259.000\n",
            ">Predicted=259.000, Expected=229.000\n",
            ">Predicted=229.000, Expected=203.000\n",
            ">Predicted=203.000, Expected=229.000\n",
            ">Predicted=229.000, Expected=242.000\n",
            ">Predicted=242.000, Expected=233.000\n",
            ">Predicted=233.000, Expected=267.000\n",
            ">Predicted=267.000, Expected=269.000\n",
            ">Predicted=269.000, Expected=270.000\n",
            ">Predicted=270.000, Expected=315.000\n",
            ">Predicted=315.000, Expected=364.000\n",
            ">Predicted=364.000, Expected=347.000\n",
            ">Predicted=347.000, Expected=312.000\n",
            ">Predicted=312.000, Expected=274.000\n",
            ">Predicted=274.000, Expected=237.000\n",
            ">Predicted=237.000, Expected=278.000\n",
            "MAE_base: 0.089\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SARIMA Model**"
      ],
      "metadata": {
        "id": "-v8AcoZ0ZStL"
      },
      "id": "-v8AcoZ0ZStL"
    },
    {
      "cell_type": "code",
      "source": [
        "df_airline = pd.read_excel('/content/Airlines+Data.xlsx')"
      ],
      "metadata": {
        "id": "BFQBCQ6uAaI3"
      },
      "id": "BFQBCQ6uAaI3",
      "execution_count": 273,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_airline.set_index('Month',inplace=True)"
      ],
      "metadata": {
        "id": "i6bpjYfKBFNp"
      },
      "id": "i6bpjYfKBFNp",
      "execution_count": 274,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_airline.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "Gt4ZcoN4BXuB",
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      },
      "id": "Gt4ZcoN4BXuB",
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Passengers\n",
              "Month                 \n",
              "1995-01-01         112\n",
              "1995-02-01         118\n",
              "1995-03-01         132\n",
              "1995-04-01         129\n",
              "1995-05-01         121"
            ],
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              "\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#performing test to check whether data is stationary or not\n",
        "from statsmodels.tsa.stattools import adfuller\n",
        "\n",
        "def adf_test(series):\n",
        "    result=adfuller(series)\n",
        "    print('ADF Statistics: {}'.format(result[0]))\n",
        "    print('p- value: {}'.format(result[1]))\n",
        "    if result[1] <= 0.05:\n",
        "        print(\"strong evidence against the null hypothesis, 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 \")"
      ],
      "metadata": {
        "id": "ynxVYqTqtXs3"
      },
      "id": "ynxVYqTqtXs3",
      "execution_count": 276,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "adf_test(df_airline['Passengers'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6sshRi3KHtDt",
        "outputId": "6fbfd3a9-9e40-4403-f9d9-233e9bcf89c4"
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      "id": "6sshRi3KHtDt",
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistics: 1.3402479596467076\n",
            "p- value: 0.9968250481137263\n",
            "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Use Techniques Differencing 1st time\n",
        "df_airline['Passengers First Difference']=df_airline['Passengers']-df_airline['Passengers'].shift(12)"
      ],
      "metadata": {
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              "            Passengers  Passengers First Difference\n",
              "Month                                              \n",
              "1995-11-01         104                          NaN\n",
              "1995-12-01         118                          NaN\n",
              "1996-01-01         115                          3.0\n",
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              "1996-03-01         141                          9.0\n",
              "1996-04-01         135                          6.0\n",
              "1996-05-01         125                          4.0\n",
              "1996-06-01         149                         14.0\n",
              "1996-07-01         170                         22.0\n",
              "1996-08-01         170                         22.0"
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        "adf_test(df_airline['Passengers First Difference'].dropna())"
      ],
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      "execution_count": 280,
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          "text": [
            "ADF Statistics: -2.6900043838623158\n",
            "p- value: 0.07578397625851772\n",
            "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Use Techniques Differencing 2nd time\n",
        "df_airline['Passengers Second Difference']=df_airline['Passengers First Difference']-df_airline['Passengers First Difference'].shift(12)"
      ],
      "metadata": {
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      "execution_count": 281,
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      "execution_count": 282,
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            "text/plain": [
              "            Passengers  Passengers First Difference  \\\n",
              "Month                                                 \n",
              "1996-09-01         158                         22.0   \n",
              "1996-10-01         133                         14.0   \n",
              "1996-11-01         114                         10.0   \n",
              "1996-12-01         140                         22.0   \n",
              "1997-01-01         145                         30.0   \n",
              "1997-02-01         150                         24.0   \n",
              "1997-03-01         178                         37.0   \n",
              "1997-04-01         163                         28.0   \n",
              "1997-05-01         172                         47.0   \n",
              "1997-06-01         178                         29.0   \n",
              "\n",
              "            Passengers Second Difference  \n",
              "Month                                     \n",
              "1996-09-01                           NaN  \n",
              "1996-10-01                           NaN  \n",
              "1996-11-01                           NaN  \n",
              "1996-12-01                           NaN  \n",
              "1997-01-01                          27.0  \n",
              "1997-02-01                          16.0  \n",
              "1997-03-01                          28.0  \n",
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              "1997-05-01                          43.0  \n",
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      ],
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      "execution_count": 283,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistics: -2.1094801421381764\n",
            "p- value: 0.24071971540604048\n",
            "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Use Techniques Differencing 2nd time\n",
        "df_airline['Passengers Third Difference']=df_airline['Passengers Second Difference']-df_airline['Passengers Second Difference'].shift(12)"
      ],
      "metadata": {
        "id": "QU-mGoomM63g"
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      "id": "QU-mGoomM63g",
      "execution_count": 284,
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      "id": "aaNMd0KXNCvn",
      "execution_count": 285,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Passengers  Passengers First Difference  \\\n",
              "Month                                                 \n",
              "1997-07-01         199                         29.0   \n",
              "1997-08-01         199                         29.0   \n",
              "1997-09-01         184                         26.0   \n",
              "1997-10-01         162                         29.0   \n",
              "1997-11-01         146                         32.0   \n",
              "1997-12-01         166                         26.0   \n",
              "1998-01-01         171                         26.0   \n",
              "1998-02-01         180                         30.0   \n",
              "1998-03-01         193                         15.0   \n",
              "1998-04-01         181                         18.0   \n",
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              "Month                                                                  \n",
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              "1997-09-01                           4.0                          NaN  \n",
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              "1997-11-01                          22.0                          NaN  \n",
              "1997-12-01                           4.0                          NaN  \n",
              "1998-01-01                          -4.0                        -31.0  \n",
              "1998-02-01                           6.0                        -10.0  \n",
              "1998-03-01                         -22.0                        -50.0  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 285
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adf_test(df_airline['Passengers Third Difference'].dropna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QQnL3cpmNG4u",
        "outputId": "bd957e75-5db9-4e02-878b-5859c4aa167e"
      },
      "id": "QQnL3cpmNG4u",
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ADF Statistics: -4.893824055490128\n",
            "p- value: 3.5852657613944055e-05\n",
            "strong evidence against the null hypothesis, reject the null hypothesis. Data has no unit root and is stationary\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We can perform ARIMA on Passengers Third Difference data as this is stationary."
      ],
      "metadata": {
        "id": "eNO6vDquJQ7W"
      },
      "id": "eNO6vDquJQ7W"
    },
    {
      "cell_type": "code",
      "source": [
        "acf = plot_acf(df_airline[\"Passengers Third Difference\"].dropna())                #q= 2 / 12"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Y_PtioN7-4Mv",
        "outputId": "6ca3619d-23af-42fc-ba8f-ee0687431c0a"
      },
      "id": "Y_PtioN7-4Mv",
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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n655+Xjp8nDULZ7F2ZRdtGvNYRGqkIGiwbCH69d97LEuuEFYtF4bOJ773Art6B8kVQjra2zhv8Ww+ct2FEwoDhYmIlFMQNMiR4zkOHYt+/Y/n0s6te/rZ1TtINg6LbCFkV+8gW/f0c9nq+WNsHUkqTESktSQyZrGMTxhGPUkOZgv8y8FjHDk+vhAAeOnw8dOOGHKFkJcOHx/3+5eGiXNqmIhIeikI6iAInf39w/x0zxFG8gHhGXTOs2bhLDraT/1zdbS3sWbhrHG/RhJhIiKtR01DkygInUMDIxw4Olzz1T9rV3Zx3uLZbHvlVci00zmtnfMWz2btyq5xv0YxTLIlYTDRMBGR1qMjgkkQhM6+/mF++soRXj48lMgloG1txkeuu5DZ27/LjH/9B37vLedPuG2/GCYUcuAhnfE5gomEiYi0HgVBgoLQ2XtkiJ++coRXDg8lfgdwW5vRcXgXM17+Ry5bPX/CJ3iTCBMRaT1qGkpAIQg5ODDCwaMjTd/9QzFMOLyLy1Z/uNHVEZEmoCCoQSEIOXB0hIMDIxpiUESmLAXBGVAAiEgrURBMgDvkgpCf7ulXAIhIy1AQjEM+CDnQP8JgtoC7pzoE1EWFSOtJJAjM7FrgL4AM8CV3v6ds/e8D7wMKQB/w2+7+crwuAJ6Li77i7tcnUackFAPg4MAIQeh4yof5UhcVIq2p5iAwswzweeBqYC/wpJltdPftJcV+CvS4+5CZ/RfgU8BvxuuG3X1trfVIUq4QcuDoMIcGsgRncBdwq0qivyMRaT5J3EdwObDL3Xe7ew54CLihtIC7/8jdh+LZx4EVCbzvpNjfP8zWPf3s7x9RCJRRFxUirSmJIFgO7CmZ3xsvq+Zm4Hsl89PNbIuZPW5mb6+2kZndEpfb0tfXV1uNR9E/lFcAVJFEf0ci0nzqemexmf0noAf4s5LFq929B3gX8FkzO7fStu5+r7v3uHtPd3d3HWor5dRFhUhrSiII9gErS+ZXxMtOYWZXAXcA17t7trjc3ffFz7uBzcClCdRJJoG6qBBpTUkEwZPA+WZ2tpl1ADcCG0sLmNmlwBeJQqC3ZPl8M+uMpxcBbwBKTzJLk6m1vyMRaT41XzXk7gUzuxV4lOjy0Q3uvs3M7ga2uPtGoqag2cC3zAxOXiZ6IfBFMwuJQumesquNRERkkiVyH4G7PwI8UrbszpLpq6ps90/ALyRRBxEROTPqhlpEJOUUBCIiKae+hqTu1F+RSHNREEhdqb8ikeajpiGpq9L+ipxT+ysSkcZQEEhdqb8ikeajIJC6Un9FIs1HQSB1pf6KRJqPgkDqSv0ViTQfBYHUnforEmkuCgIRkZRTEIiIpJyCQEQk5RQEIiIppyAQEUk5BYGISMqp0zmZctR7qUiyFAQypaj3UpHkqWlIphT1XiqSPAWBTCnqvVQkeQoCmVLUe6lI8hIJAjO71sx2mNkuM7utwvpOM3s4Xv+Ema0pWXd7vHyHmb01ifpI61LvpSLJqzkIzCwDfB64DrgIeKeZXVRW7GbgiLufB3wG+GS87UXAjcDFwLXA/4pfT6Qi9V4qkjxz99pewOz1wF3u/tZ4/nYAd//TkjKPxmV+YmbtwEGgG7ittGxpudHec8HqC/3qj2yYcF23PrMVgLWXrK1aZigXUAjDiut2bn8egPMv+vkJv3dSr6E6JFcHkalmxrQM0zJn/vv9m+//5afcvad8eRKXjy4H9pTM7wWuqFbG3QtmdhRYGC9/vGzb5ZXexMxuAW4BmL303DOq6GgBMB5J7HRqfQ3VIZnt3Z0Xd+2GTAfLli1ldmcGs4kdVbRCILbCv0F1qN2UuY/A3e8F7gXo6enxh3/n9ZPyPtv3D3B0OD8pry3NoXgvQji9CzLt9B3LMm/GxO9F+MA3bgfgzj/ceMZ1qfU1Gr296lDfOpzTPYslc6ef0esDfPP9lZcncbJ4H7CyZH5FvKximbhpaB5weJzbiiSqeC8C7R1gbboXQVIviSB4EjjfzM42sw6ik7/lkbYRWB9P/wbwQ49OTmwEboyvKjobOB/45wTqJFKV7kUQOVXNTUNxm/+twKNABtjg7tvM7G5gi7tvBO4DHjCzXcBrRGFBXO6bwHagAHzA3YNa6yQymuK9CNmSMNC9CJJmiZwjcPdHgEfKlt1ZMj0C/Mcq234c+HgS9RAZj+K9COX9FeleBEmrKXOyWCQpxXsR1IOpSERBIKnU1mZctno+l62e3+iqiDSc+hoSEUk5BYGISMopCEREUk5BIHIGwtDJLTyP4dVv4OmXjxCGtfXZJdJIOlksMkHFLioGL3o7ZNr53A93arhMmdJ0RCAyQeqiQlqNgkBkgtRFhbQaBYHIBGm4TGk1CgKRCSp2UdHZ3oaBhsuUKU8ni0UmSF1USKtREIicAXVRIa1ETUMiIimnIBARabBG36CopiERkQZqhhsUdUQgItJAzXCDooJARKSBmuEGRQWBiEgDNcMNigoCEZEGaoYbFHWyWESkRsWrfoLZS3j65SMTusGwGW5QrCkIzGwB8DCwBngJeIe7Hykrsxb4AjAXCICPu/vD8bqvAL8CHI2L3+TuW2upk4hIPSVx1U+jb1CstWnoNmCTu58PbIrnyw0B73H3i4Frgc+aWekxz39397XxQyEgIlNKM1z1U6tag+AG4P54+n7g7eUF3P1Fd98ZT+8HeoHuGt9XRARo/M1YzXDVT61qDYIl7n4gnj4ILBmtsJldDnQAPytZ/HEze9bMPmNmnaNse4uZbTGzLX19fTVWu7rVC2eyYFbHpL2+iCSntFlm+Ow38bkf7uQT33uhrmHQDFf91GrMIDCzH5jZ8xUeN5SWc3cHqn76ZrYUeAB4r7sX4/N24OeAfwssAD5cbXt3v9fde9y9p7t78g4oZnW2c8FZc/jFFfNYOFuBINLMmqFZphmu+qnVmCeL3f2qauvM7JCZLXX3A/GOvrdKubnA/wXucPfHS167eDSRNbMvA38wodpPolmd7fybJXMYml9g35FhDh/P4RqfXKSpjNYsU68Tr81w1U+tar18dCOwHrgnfv6b8gJm1gH8NfBVd/922bpiiBjR+YXna6xP4mZ2tHP+kjkszykQRJpNsVkmWxIGjWiWafRVP7Wq9RzBPcDVZrYTuCqex8x6zOxLcZl3AG8GbjKzrfFjbbzu62b2HPAcsAj4WI31mTTFQLhkRRfdczqwqRP2Ii2rFZplmkFNRwTufhi4ssLyLcD74umvAV+rsv1bann/RpjRkeG8xXNY3hWwr3+IVwd1hCBnppabkCSSRLOM/g66s/iMFQNhxfyAvUeGeXUwq0CQcWuGrodbRS3NMvo7RNTXUI2mT8ucOBTtntOpJiMZl2a42kX0dyhSECSkNBAWz1UgyOha4SakVqC/Q0RBkLDp0zKc2x0FwpK5naTo6FImoBVuQmoF+jtEFASTZPq0DOd0z+YSBYJUoKtdmoP+DhGdLJ5kxUBYPn8G+/tH6B0Yoc5doUgTaoWbkFqB/g4RBUGddLZnOHvRLJZ1Tad3IEvvsRFyBSVCmk31m5CS0AyXburvoCCou872DCsXzGTF/Bm8djzHoYEsR4fzja6WSN3p0s3moXMEDWJmLJzdyUXL5nLpqi6WdU1nWkZffkkPXbrZPBQETWD6tAyrF87islXzOW/xbObO0IGatD5dutk8tMdpIm1tRvecTrrndDKcCzg0MELfYJZCoHMJ0nqapcM40RFB05rRkWHNoln80qr5nLt4FnOmK7OltejSzeahvUuTa2szFs+ZzuI50zmeLXBoYITDx3M6SpApT5duNg8FwRQyq7Odc7pnc043ZAsBI/mQbD56HikEjMTTgW5UkClCl242BwXBFNXZnqGzPQMzpp22Llc4GQzZfHgiIEYKgY4kROQ0CoIW1NHeRkd7G3OnVw+JYkBkCyefy6/gEBlLM9wQJrVTEKRMMSSYfvq6MHSyhfBks1PZs5qcpJRuCGsdCgI5oa3NmNGRYUZHpuL6fBCeOILIFUIKgVMIQwqhnzat0Gh9p9wQBqfcEKY2/6lFQSDjNi3TxrRMG7M7x/7auDv5OBAKYTE0Tk4HoRO44+64Q+jgOGEIYTzUW+geLS95dqJfosqZxhvthjAFwdSiIJBJYWZ0tBebByofYdTKxxgbdDxDh9aaJ8U6ePx+jp943+L8yemTYeYebeQ4uSAkHzj5QkguiM7V5OPn0QKv0e3zuiGsddQUBGa2AHgYWAO8BLzD3Y9UKBcAz8Wzr7j79fHys4GHgIXAU8C73T1XS50kPWyMYeDqM0rc5L5JIQ6JXBwSxYAYyQd8+DvPNrR9vnhD2K7eQXKFkA7dEDZl1XpEcBuwyd3vMbPb4vkPVyg37O5rKyz/JPAZd3/IzP4KuBn4Qo11EmkZ7Zk22jOcdt5m0wuH2FnWPr+77zi7+gb5+eVz69LFuW4Iax21BsENwLp4+n5gM5WD4DQW/Zx7C/Cuku3vQkEgMqZt+wcYzgWnLBvJBwwM5/ml1QsYyQcMZgscGykwOFLgeK4wrqayidINYa2h1iBY4u4H4umDwJIq5aab2RagANzj7t8lag7qd/dCXGYvsLzG+oikwsXL5jKjI8NQSRjM6Mhw0bK5QNSj7fRpGRbN7gSi8wmDuZPBMJjNa2AkOWHMIDCzHwBnVVh1R+mMu7uZVftmrXb3fWZ2DvBDM3sOODqRiprZLcAtAKtWrZrIpiItZ90Fi1m7soute/oZzgXM6MiwdmUX6y5YXLF8W5sxd/q0U24yHMlH2wWhMy1j5HXXeWqNGQTuflW1dWZ2yMyWuvsBM1sK9FZ5jX3x824z2wxcCnwH6DKz9vioYAWwb5R63AvcC9DT06NvrKRaps144OYr2Lyjl+37B7ho2VzWXbCYzATa56dPy8SXBMNlq+ZzZChH32CW/qH8pDQjSfOqtWloI7AeuCd+/pvyAmY2Hxhy96yZLQLeAHwqPoL4EfAbRFcOVdxeRCrLtBlXXriEKy+s1iI7fm1t0Yh5C2d3kiuEHD6epe9YluPZYOyNZcqrdTyCe4CrzWwncFU8j5n1mNmX4jIXAlvM7BngR0TnCLbH6z4M/L6Z7SI6Z3BfjfURkRp1tLexdN4MfnFFF7+4Yh5L500vuSdEWlFNRwTufhi4ssLyLcD74ul/An6hyva7gctrqYOITJ5Zne3M6mxn9cKZ9A/l6RvMcuR4Tnd2txjdWSwiYzIz5s/qYP6sDgpByKuDOV4dzHJspDD2xtL0FAQiMiHtmTbOmjeds+ZNZzgX0Hcsy8BInqFcoM4GpygFgYicsRkdGVYtnHliPlsIGM4FDOUChvMnpxUQzU1BICKJKY6c1zXz1OUKiOamIBCRSTdWQAzHw6oWe1/NFgLd+VxHCgIRaZgTAVFhXRhGXXQXh1HNBSHZfFASFqHG4E6IgkBEmlJbmzG9LeozqZog9BNHEPnACT0aACkMo+cgPDk4UhgSPXs0ip5apU5SEIjIlJUZY3jV0biXh4VTCKLhVvPByaFY88HpQ7G2GgWBiKSSmTEtY4xywFFRcRjWkyFxMjyKQ62WD7NaXOZOPDRr5TKN6uNJQSAiMgHFYVg7au6h53SVgiMsWdbZnvx7goJAJLWC0BnqOofcrCVseuHQhHsvleSZGRmDzCQPgVpOQSCSQkHovPu+J+g7/9fwtnZ+98GfsnZlFw/cfIXCIIUm5zhDRJra5h29bN3Tj2c6wNoYygVs3dPP5h0VhxSRFqcgEEmhSmMeD+cCtu8faFCNpJEUBCIpVBzzuFTpmMeSLgoCkRQqjnk8syM6LTlzjDGPpbXpZLFICiUx5rG0DgWBSEolOeaxTG1qGhIRSTkFgYhIyikIRERSTkEgIpJyCgIRkZQzb1S/pzUwsz7g5TPcfBHwaoLVmQyqYzKmQh1hatRTdUxGo+u42t27yxdOySCohZltcfeeRtdjNKpjMqZCHWFq1FN1TEaz1lFNQyIiKacgEBFJuTQGwb2NrsA4qI7JmAp1hKlRT9UxGU1Zx9SdIxARkVOl8YhARERKKAhERFKuZYPAzK41sx1mtsvMbquwvtPMHo7XP2Fma+pcv5Vm9iMz225m28zsv1Uos87MjprZ1vhxZz3rGNfhJTN7Lma7hMcAAARuSURBVH7/LRXWm5l9Lv4cnzWzy+pcvwtKPp+tZjZgZh8sK9OQz9HMNphZr5k9X7JsgZk9ZmY74+f5VbZdH5fZaWbr61zHPzOzf4n/nn9tZl1Vth31uzHJdbzLzPaV/E3fVmXbUfcDk1zHh0vq95KZba2ybV0+x1G5e8s9gAzwM+AcoAN4BriorMx/Bf4qnr4ReLjOdVwKXBZPzwFerFDHdcDfNfizfAlYNMr6twHfAwx4HfBEg//uB4lummn45wi8GbgMeL5k2aeA2+Lp24BPVthuAbA7fp4fT8+vYx2vAdrj6U9WquN4vhuTXMe7gD8Yx/dh1P3AZNaxbP2ngTsb+TmO9mjVI4LLgV3uvtvdc8BDwA1lZW4A7o+nvw1caWZ1G5XD3Q+4+9Px9DHgBWB5vd4/QTcAX/XI40CXmS1tUF2uBH7m7md613mi3P3HwGtli0u/d/cDb6+w6VuBx9z9NXc/AjwGXFuvOrr79929EM8+DqyYjPceryqf43iMZz+QiNHqGO9X3gE8OBnvnYRWDYLlwJ6S+b2cvpM9USb+0h8FFtaldmXiZqlLgScqrH69mT1jZt8zs4vrWrGIA983s6fM7JYK68fzWdfLjVT/z9boz7FoibsfiKcPApVGhWmmz/S3iY74KhnruzHZbo2brzZUaWJrls/xTcAhd99ZZX2jP8eWDYIpw8xmA98BPujuA2WrnyZq5rgE+Evgu/WuH/BGd78MuA74gJm9uQF1GJOZdQDXA9+qsLoZPsfTeNQu0LTXb5vZHUAB+HqVIo38bnwBOBdYCxwganppVu9k9KOBhv8fa9Ug2AesLJlfES+rWMbM2oF5wOG61C5mZtOIQuDr7v5/yte7+4C7D8bTjwDTzGxRPevo7vvi517gr4kOt0uN57Ouh+uAp939UPmKZvgcSxwqNp3Fz70VyjT8MzWzm4BfBX4rDqzTjOO7MWnc/ZC7B+4eAv+7yns3w+fYDvw68HC1Mo38HItaNQieBM43s7PjX4o3AhvLymwEildj/Abww2pf+MkQtxveB7zg7n9epcxZxfMWZnY50d+rbmFlZrPMbE5xmugk4vNlxTYC74mvHnodcLSk6aOeqv7qavTnWKb0e7ce+JsKZR4FrjGz+XGTxzXxsrows2uBPwSud/ehKmXG892YzDqWnof691Xeezz7gcl2FfAv7r630spGf44nNPJM9WQ+iK5meZHoqoE74mV3E325AaYTNSPsAv4ZOKfO9XsjUbPAs8DW+PE24P3A++MytwLbiK52eBz45TrX8Zz4vZ+J61H8HEvraMDn48/5OaCnAX/rWUQ79nklyxr+ORIF0wEgT9Q+fTPReahNwE7gB8CCuGwP8KWSbX87/m7uAt5b5zruImpbL34vi1fXLQMeGe27Ucc6PhB/354l2rkvLa9jPH/afqBedYyXf6X4PSwp25DPcbSHupgQEUm5Vm0aEhGRcVIQiIiknIJARCTlFAQiIimnIBARSTkFgYhIyikIRERS7v8DjNaiqYuNktoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pacf = plot_pacf(df_airline[\"Passengers Third Difference\"].dropna())     #p= 1/10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "EPALQefr-5IE",
        "outputId": "ed8b9fa0-2ef0-4574-c18e-ddb6a57ee89f"
      },
      "id": "EPALQefr-5IE",
      "execution_count": 287,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime,timedelta\n",
        "train_dataset_end=datetime(1999,12,1)\n",
        "test_dataset_end=datetime(2002,12,1)"
      ],
      "metadata": {
        "id": "t0fFPH-6IRWU"
      },
      "id": "t0fFPH-6IRWU",
      "execution_count": 289,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data=df_airline[:train_dataset_end]\n",
        "test_data=df_airline[train_dataset_end+timedelta(days=1):test_dataset_end]"
      ],
      "metadata": {
        "id": "dmKzkjXcJVY3"
      },
      "id": "dmKzkjXcJVY3",
      "execution_count": 290,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## create a SARIMA model as we are working with seasonal data\n",
        "from statsmodels.tsa.statespace.sarimax import SARIMAX"
      ],
      "metadata": {
        "id": "jxGXV35lNeyH"
      },
      "id": "jxGXV35lNeyH",
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_SARIMA=SARIMAX(train_data['Passengers'],order=(10,0,2),seasonal_order=(0,3,0,12))"
      ],
      "metadata": {
        "id": "2fFkascbNfzA"
      },
      "id": "2fFkascbNfzA",
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_SARIMA_fit=model_SARIMA.fit()"
      ],
      "metadata": {
        "id": "xZrZ7rdtOH-T"
      },
      "id": "xZrZ7rdtOH-T",
      "execution_count": 293,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_SARIMA_fit.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "oirpQv3IOPi-",
        "outputId": "c7392832-8b75-42ca-ab75-4882fc60f5a2"
      },
      "id": "oirpQv3IOPi-",
      "execution_count": 294,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                      SARIMAX Results                                       \n",
              "============================================================================================\n",
              "Dep. Variable:                           Passengers   No. Observations:                   60\n",
              "Model:             SARIMAX(10, 0, 2)x(0, 3, [], 12)   Log Likelihood                -106.783\n",
              "Date:                              Mon, 29 Aug 2022   AIC                            239.565\n",
              "Time:                                      15:51:55   BIC                            254.880\n",
              "Sample:                                  01-01-1995   HQIC                           243.628\n",
              "                                       - 12-01-1999                                         \n",
              "Covariance Type:                                opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1          0.5799      0.629      0.923      0.356      -0.652       1.812\n",
              "ar.L2          0.4939      0.714      0.691      0.489      -0.906       1.894\n",
              "ar.L3         -0.4802      0.616     -0.780      0.436      -1.687       0.727\n",
              "ar.L4          0.1986      0.546      0.363      0.716      -0.872       1.270\n",
              "ar.L5         -0.1636      0.484     -0.338      0.735      -1.112       0.784\n",
              "ar.L6         -0.0070      0.479     -0.015      0.988      -0.945       0.931\n",
              "ar.L7         -0.0392      0.584     -0.067      0.947      -1.184       1.106\n",
              "ar.L8         -0.0790      0.319     -0.248      0.804      -0.704       0.546\n",
              "ar.L9          0.3844      0.615      0.625      0.532      -0.821       1.590\n",
              "ar.L10        -0.4762      0.439     -1.085      0.278      -1.336       0.384\n",
              "ma.L1          0.2005     12.472      0.016      0.987     -24.245      24.646\n",
              "ma.L2         -0.7815     10.164     -0.077      0.939     -20.702      19.139\n",
              "sigma2        98.1746   1201.890      0.082      0.935   -2257.487    2453.836\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   1.22   Jarque-Bera (JB):                 1.58\n",
              "Prob(Q):                              0.27   Prob(JB):                         0.45\n",
              "Heteroskedasticity (H):               0.92   Skew:                            -0.16\n",
              "Prob(H) (two-sided):                  0.91   Kurtosis:                         1.78\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>              <td>Passengers</td>            <th>  No. Observations:  </th>    <td>60</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(10, 0, 2)x(0, 3, [], 12)</td> <th>  Log Likelihood     </th> <td>-106.783</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                    <td>Mon, 29 Aug 2022</td>         <th>  AIC                </th>  <td>239.565</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                        <td>15:51:55</td>             <th>  BIC                </th>  <td>254.880</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                     <td>01-01-1995</td>            <th>  HQIC               </th>  <td>243.628</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                           <td>- 12-01-1999</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>    0.5799</td> <td>    0.629</td> <td>    0.923</td> <td> 0.356</td> <td>   -0.652</td> <td>    1.812</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2</th>  <td>    0.4939</td> <td>    0.714</td> <td>    0.691</td> <td> 0.489</td> <td>   -0.906</td> <td>    1.894</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L3</th>  <td>   -0.4802</td> <td>    0.616</td> <td>   -0.780</td> <td> 0.436</td> <td>   -1.687</td> <td>    0.727</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L4</th>  <td>    0.1986</td> <td>    0.546</td> <td>    0.363</td> <td> 0.716</td> <td>   -0.872</td> <td>    1.270</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L5</th>  <td>   -0.1636</td> <td>    0.484</td> <td>   -0.338</td> <td> 0.735</td> <td>   -1.112</td> <td>    0.784</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L6</th>  <td>   -0.0070</td> <td>    0.479</td> <td>   -0.015</td> <td> 0.988</td> <td>   -0.945</td> <td>    0.931</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L7</th>  <td>   -0.0392</td> <td>    0.584</td> <td>   -0.067</td> <td> 0.947</td> <td>   -1.184</td> <td>    1.106</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L8</th>  <td>   -0.0790</td> <td>    0.319</td> <td>   -0.248</td> <td> 0.804</td> <td>   -0.704</td> <td>    0.546</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L9</th>  <td>    0.3844</td> <td>    0.615</td> <td>    0.625</td> <td> 0.532</td> <td>   -0.821</td> <td>    1.590</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L10</th> <td>   -0.4762</td> <td>    0.439</td> <td>   -1.085</td> <td> 0.278</td> <td>   -1.336</td> <td>    0.384</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>  <td>    0.2005</td> <td>   12.472</td> <td>    0.016</td> <td> 0.987</td> <td>  -24.245</td> <td>   24.646</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2</th>  <td>   -0.7815</td> <td>   10.164</td> <td>   -0.077</td> <td> 0.939</td> <td>  -20.702</td> <td>   19.139</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th> <td>   98.1746</td> <td> 1201.890</td> <td>    0.082</td> <td> 0.935</td> <td>-2257.487</td> <td> 2453.836</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>1.22</td> <th>  Jarque-Bera (JB):  </th> <td>1.58</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.27</td> <th>  Prob(JB):          </th> <td>0.45</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>0.92</td> <th>  Skew:              </th> <td>-0.16</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.91</td> <th>  Kurtosis:          </th> <td>1.78</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ]
          },
          "metadata": {},
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.tail()"
      ],
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      "id": "Klq_B_GWOPcN",
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Passengers  Passengers First Difference  \\\n",
              "Month                                                 \n",
              "2002-08-01         405                         58.0   \n",
              "2002-09-01         355                         43.0   \n",
              "2002-10-01         306                         32.0   \n",
              "2002-11-01         271                         34.0   \n",
              "2002-12-01         306                         28.0   \n",
              "\n",
              "            Passengers Second Difference  Passengers Third Difference  \n",
              "Month                                                                  \n",
              "2002-08-01                           4.0                        -29.0  \n",
              "2002-09-01                         -10.0                        -41.0  \n",
              "2002-10-01                         -13.0                        -40.0  \n",
              "2002-11-01                           0.0                        -11.0  \n",
              "2002-12-01                         -21.0                        -42.0  "
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              "      <th>Passengers</th>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "##prediction\n",
        "pred_start_date=test_data.index[0]\n",
        "pred_end_date=test_data.index[-1]\n",
        "print(pred_start_date)\n",
        "print(pred_end_date)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tk-kLbHROPVN",
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      },
      "id": "Tk-kLbHROPVN",
      "execution_count": 296,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2000-01-01 00:00:00\n",
            "2002-12-01 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_Sarima=model_SARIMA_fit.predict(start=datetime(2000,1,1),end=datetime(2002,12,1))"
      ],
      "metadata": {
        "id": "q8U9gdU5OPPq"
      },
      "id": "q8U9gdU5OPPq",
      "execution_count": 297,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MAE_Sarima = MAE(pred_Sarima, test_data['Passengers'])\n",
        "MAE_Sarima "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CH0ZH4ajO7pl",
        "outputId": "2c60a18d-40b6-4f2a-fbf6-50ae257888fe"
      },
      "id": "CH0ZH4ajO7pl",
      "execution_count": 298,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.16040558523656834"
            ]
          },
          "metadata": {},
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compare the results \n",
        "\n",
        "data = {\"MODEL\":pd.Series([\"MAE_linear\",\"MAE_Exp\",\"MAE_Quad\",\"MAE_add_sea\",\"MAE_add_sea_quad\",\"MAE_Mult_sea\",\"MAE_Mult_add_sea\",\"MAE_ses\",\"MAE_hw\",\"MAE_hwe_add_add\",\"MAE__hwe_mul_add\",\"MAE_base\",\"MAE_SARIMA\"]),\"MAE_Values\":pd.Series([mae_linear,mae_Exp,mae_Quad,mae_add_sea,mae_add_sea_quad,mae_Mult_sea,mae_Mult_add_sea,MAE_ses,MAE_hw,MAE__hwe_add_add,MAE__hwe_mul_add,MAE_base,MAE_Sarima])}\n",
        "table_mae=pd.DataFrame(data)\n",
        "table_mae.sort_values(['MAE_Values'])"
      ],
      "metadata": {
        "id": "3B3cXLXkVuRk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "13b6586a-196e-48e9-b8e4-3596c6584a6c"
      },
      "execution_count": 299,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               MODEL  MAE_Values\n",
              "6   MAE_Mult_add_sea    0.024187\n",
              "10  MAE__hwe_mul_add    0.028211\n",
              "9    MAE_hwe_add_add    0.054999\n",
              "4   MAE_add_sea_quad    0.055711\n",
              "11          MAE_base    0.089354\n",
              "1            MAE_Exp    0.111025\n",
              "2           MAE_Quad    0.118382\n",
              "0         MAE_linear    0.128759\n",
              "7            MAE_ses    0.134113\n",
              "12        MAE_SARIMA    0.160406\n",
              "8             MAE_hw    0.235307\n",
              "3        MAE_add_sea    0.660196\n",
              "5       MAE_Mult_sea    0.721824"
            ],
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              "\n",
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              "      <th>6</th>\n",
              "      <td>MAE_Mult_add_sea</td>\n",
              "      <td>0.024187</td>\n",
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              "      <td>0.028211</td>\n",
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              "      <th>9</th>\n",
              "      <td>MAE_hwe_add_add</td>\n",
              "      <td>0.054999</td>\n",
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              "      <th>4</th>\n",
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              "      <th>11</th>\n",
              "      <td>MAE_base</td>\n",
              "      <td>0.089354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MAE_Exp</td>\n",
              "      <td>0.111025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MAE_Quad</td>\n",
              "      <td>0.118382</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MAE_linear</td>\n",
              "      <td>0.128759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>MAE_ses</td>\n",
              "      <td>0.134113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>MAE_SARIMA</td>\n",
              "      <td>0.160406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>MAE_hw</td>\n",
              "      <td>0.235307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MAE_add_sea</td>\n",
              "      <td>0.660196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>MAE_Mult_sea</td>\n",
              "      <td>0.721824</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 299
        }
      ],
      "id": "3B3cXLXkVuRk"
    },
    {
      "cell_type": "markdown",
      "source": [
        "From all the models created we can see here ***Multiplicative Additive Seasonality*** giving us best results as the Test MAE is the lowest for this data.\n",
        "\n",
        "- Let us build a model and forecast year of 2003."
      ],
      "metadata": {
        "id": "CDCC_AJikFXE"
      },
      "id": "CDCC_AJikFXE"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predict on new data**"
      ],
      "metadata": {
        "id": "IYo9jaOnGbiU"
      },
      "id": "IYo9jaOnGbiU"
    },
    {
      "cell_type": "code",
      "source": [
        "Test = airline1.tail(12)"
      ],
      "metadata": {
        "id": "GrRJYvwMkEwB"
      },
      "id": "GrRJYvwMkEwB",
      "execution_count": 300,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Test.to_csv('predict_data.csv')\n",
        "#Editted the test data a little to forecast passengers for year 2003"
      ],
      "metadata": {
        "id": "KECbHV2ANCJM"
      },
      "execution_count": 301,
      "outputs": [],
      "id": "KECbHV2ANCJM"
    },
    {
      "cell_type": "code",
      "source": [
        "#We will try to predict the data for the whole yearr of 2022\n",
        "predict_data = pd.read_csv('/content/predict_data.csv')\n",
        "predict_data.head()"
      ],
      "metadata": {
        "id": "WbNbUHG-NB8I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "outputId": "acf6e213-46d4-4e02-d6e3-8d65ddbcdd4c"
      },
      "execution_count": 302,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Month  year  month_Apr  month_Aug  month_Dec  month_Feb  month_Jan  \\\n",
              "0  01-01-2003  2003          0          0          0          0          1   \n",
              "1  01-02-2003  2003          0          0          0          1          0   \n",
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              "   month_Jul  month_Jun  month_Mar  month_May  month_Nov  month_Oct  \\\n",
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              "4          0          0          0          1          0          0   \n",
              "\n",
              "   month_Sep    t  t_sqr  \n",
              "0          0   97   9409  \n",
              "1          0   98   9604  \n",
              "2          0   99   9801  \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      "cell_type": "code",
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12 entries, 0 to 11\n",
            "Data columns (total 16 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   Month      12 non-null     object\n",
            " 1   year       12 non-null     int64 \n",
            " 2   month_Apr  12 non-null     int64 \n",
            " 3   month_Aug  12 non-null     int64 \n",
            " 4   month_Dec  12 non-null     int64 \n",
            " 5   month_Feb  12 non-null     int64 \n",
            " 6   month_Jan  12 non-null     int64 \n",
            " 7   month_Jul  12 non-null     int64 \n",
            " 8   month_Jun  12 non-null     int64 \n",
            " 9   month_Mar  12 non-null     int64 \n",
            " 10  month_May  12 non-null     int64 \n",
            " 11  month_Nov  12 non-null     int64 \n",
            " 12  month_Oct  12 non-null     int64 \n",
            " 13  month_Sep  12 non-null     int64 \n",
            " 14  t          12 non-null     int64 \n",
            " 15  t_sqr      12 non-null     int64 \n",
            "dtypes: int64(15), object(1)\n",
            "memory usage: 1.6+ KB\n"
          ]
        }
      ],
      "id": "xln8ursuNB2z"
    },
    {
      "cell_type": "code",
      "source": [
        "#Convert Month column dtype to datetime64\n",
        "predict_data['Month'] = predict_data['Month'].astype(\"datetime64[ns]\")"
      ],
      "metadata": {
        "id": "nCdoCx_yNBrg"
      },
      "execution_count": 304,
      "outputs": [],
      "id": "nCdoCx_yNBrg"
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        "predict_data.info()"
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      "execution_count": 305,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12 entries, 0 to 11\n",
            "Data columns (total 16 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   Month      12 non-null     datetime64[ns]\n",
            " 1   year       12 non-null     int64         \n",
            " 2   month_Apr  12 non-null     int64         \n",
            " 3   month_Aug  12 non-null     int64         \n",
            " 4   month_Dec  12 non-null     int64         \n",
            " 5   month_Feb  12 non-null     int64         \n",
            " 6   month_Jan  12 non-null     int64         \n",
            " 7   month_Jul  12 non-null     int64         \n",
            " 8   month_Jun  12 non-null     int64         \n",
            " 9   month_Mar  12 non-null     int64         \n",
            " 10  month_May  12 non-null     int64         \n",
            " 11  month_Nov  12 non-null     int64         \n",
            " 12  month_Oct  12 non-null     int64         \n",
            " 13  month_Sep  12 non-null     int64         \n",
            " 14  t          12 non-null     int64         \n",
            " 15  t_sqr      12 non-null     int64         \n",
            "dtypes: datetime64[ns](1), int64(15)\n",
            "memory usage: 1.6 KB\n"
          ]
        }
      ],
      "id": "rGBzSv9GNBkd"
    },
    {
      "cell_type": "code",
      "source": [
        "#Build model on whole airline1 data with Multiplicative Additive Seasonality model\n",
        "final_model = smf.ols('log_passengers~t+month_Jan+month_Feb+month_Mar+month_Apr+month_May+month_Jun+month_Jul+month_Aug+month_Sep+month_Oct+month_Nov',data = airline1).fit()"
      ],
      "metadata": {
        "id": "kWaojoMJNBes"
      },
      "execution_count": 306,
      "outputs": [],
      "id": "kWaojoMJNBes"
    },
    {
      "cell_type": "code",
      "source": [
        "#this give us log_passengers \n",
        "pred_new  = pd.Series(final_model.predict(predict_data))\n",
        "pred_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDz_i0zCJZpI",
        "outputId": "28263d75-03ee-4dd8-bfef-90e57987529e"
      },
      "execution_count": 307,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     5.770755\n",
              "1     5.781280\n",
              "2     5.924459\n",
              "3     5.891606\n",
              "4     5.885083\n",
              "5     6.008926\n",
              "6     6.115180\n",
              "7     6.113063\n",
              "8     6.002824\n",
              "9     5.872713\n",
              "10    5.742262\n",
              "11    5.878809\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 307
        }
      ],
      "id": "JDz_i0zCJZpI"
    },
    {
      "cell_type": "code",
      "source": [
        "#Get number of passengers\n",
        "import math\n",
        "passengers = []\n",
        "for i in range(0,12):\n",
        "  passengers.append(round(math.exp(pred_new[i]),0))"
      ],
      "metadata": {
        "id": "JYWo0bKyKvb4"
      },
      "execution_count": 308,
      "outputs": [],
      "id": "JYWo0bKyKvb4"
    },
    {
      "cell_type": "code",
      "source": [
        "predict_data[\"Passengers\"] = passengers"
      ],
      "metadata": {
        "id": "-9OcX7UHNyrX"
      },
      "execution_count": 309,
      "outputs": [],
      "id": "-9OcX7UHNyrX"
    },
    {
      "cell_type": "code",
      "source": [
        "predict_data[\"forecasted_log_passengers\"] = pd.Series(pred_new)"
      ],
      "metadata": {
        "id": "rOY5H_QQJZi3"
      },
      "execution_count": 310,
      "outputs": [],
      "id": "rOY5H_QQJZi3"
    },
    {
      "cell_type": "code",
      "source": [
        "predict_data.head()"
      ],
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          "height": 357
        },
        "id": "M9H5GZwpJZdV",
        "outputId": "a1ce1b62-d772-4be0-a49d-c64165ff58a7"
      },
      "execution_count": 311,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Month  year  month_Apr  month_Aug  month_Dec  month_Feb  month_Jan  \\\n",
              "0 2003-01-01  2003          0          0          0          0          1   \n",
              "1 2003-01-02  2003          0          0          0          1          0   \n",
              "2 2003-01-03  2003          0          0          0          0          0   \n",
              "3 2003-01-04  2003          1          0          0          0          0   \n",
              "4 2003-01-05  2003          0          0          0          0          0   \n",
              "\n",
              "   month_Jul  month_Jun  month_Mar  month_May  month_Nov  month_Oct  \\\n",
              "0          0          0          0          0          0          0   \n",
              "1          0          0          0          0          0          0   \n",
              "2          0          0          1          0          0          0   \n",
              "3          0          0          0          0          0          0   \n",
              "4          0          0          0          1          0          0   \n",
              "\n",
              "   month_Sep    t  t_sqr  Passengers  forecasted_log_passengers  \n",
              "0          0   97   9409       321.0                   5.770755  \n",
              "1          0   98   9604       324.0                   5.781280  \n",
              "2          0   99   9801       374.0                   5.924459  \n",
              "3          0  100  10000       362.0                   5.891606  \n",
              "4          0  101  10201       360.0                   5.885083  "
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              "      <td>2003-01-04</td>\n",
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      "execution_count": 312,
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          "data": {
            "text/plain": [
              "        Month  year  month_Apr  month_Aug  month_Dec  month_Feb  month_Jan  \\\n",
              "7  2003-01-08  2003          0          1          0          0          0   \n",
              "8  2003-01-09  2003          0          0          0          0          0   \n",
              "9  2003-01-10  2003          0          0          0          0          0   \n",
              "10 2003-01-11  2003          0          0          0          0          0   \n",
              "11 2003-01-12  2003          0          0          1          0          0   \n",
              "\n",
              "    month_Jul  month_Jun  month_Mar  month_May  month_Nov  month_Oct  \\\n",
              "7           0          0          0          0          0          0   \n",
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              "10          0          0          0          0          1          0   \n",
              "11          0          0          0          0          0          0   \n",
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              "    month_Sep    t  t_sqr  Passengers  forecasted_log_passengers  \n",
              "7           0  104  10816       452.0                   6.113063  \n",
              "8           1  105  11025       405.0                   6.002824  \n",
              "9           0  106  11236       355.0                   5.872713  \n",
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              "      <td>108</td>\n",
              "      <td>11664</td>\n",
              "      <td>357.0</td>\n",
              "      <td>5.878809</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5cd4cece-706e-4025-8b7b-a536e066bab4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5cd4cece-706e-4025-8b7b-a536e066bab4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5cd4cece-706e-4025-8b7b-a536e066bab4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 312
        }
      ],
      "id": "QWRPnn6vcLVF"
    },
    {
      "cell_type": "markdown",
      "source": [
        "- So this our predicted airline passengers data for whole year 2003.\n",
        "\n",
        "- As we observed in various plots that Aug & Sept have fairly high passengers, we can observe a similar trend in predict data as well. "
      ],
      "metadata": {
        "id": "BL37S_kwN_oK"
      },
      "id": "BL37S_kwN_oK"
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Just to reflect accuracy of our model by plot 👇"
      ],
      "metadata": {
        "id": "nLXGhUdFdPFC"
      },
      "id": "nLXGhUdFdPFC"
    },
    {
      "cell_type": "code",
      "source": [
        "airline1['forecast_passengers']=np.exp(pd.Series(final_model.predict(airline1)))\n",
        "airline1[['forecast_passengers','Passengers']].plot(figsize=(12,8))"
      ],
      "metadata": {
        "id": "6Zixo7aAQ-Wk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "47cfa995-2a15-447b-dcfc-cbe8bcb210ba"
      },
      "execution_count": 313,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f34c3cf8f10>"
            ]
          },
          "metadata": {},
          "execution_count": 313
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "id": "6Zixo7aAQ-Wk"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vWLXLkC4kEpz"
      },
      "id": "vWLXLkC4kEpz",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "G3qRE4OOasCt"
      },
      "id": "G3qRE4OOasCt",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "name": "Forecasting Airlines Passenger Data.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}