15-timeseries-forecasting-arima.ipynb

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      "source": [
        "# tutorial source = https://www.youtube.com/watch?v=-ApHWO1XMx0&t=2260s\n",
        "\n",
        "!pip install pmdarima\n",
        "\n",
        "from pandas import read_csv\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from matplotlib import pyplot\n",
        "from math import sqrt\n",
        "\n",
        "# supress warning\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2QngQPDUYM25",
        "outputId": "cf02c864-15e3-49a7-931f-fed1e1c3bb51"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pmdarima in /usr/local/lib/python3.10/dist-packages (2.0.4)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.3.2)\n",
            "Requirement already satisfied: Cython!=0.29.18,!=0.29.31,>=0.29 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (3.0.8)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.23.5)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (1.11.4)\n",
            "Requirement already satisfied: statsmodels>=0.13.2 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (0.14.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (2.0.7)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (67.7.2)\n",
            "Requirement already satisfied: packaging>=17.1 in /usr/local/lib/python3.10/dist-packages (from pmdarima) (23.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.19->pmdarima) (2023.3.post1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22->pmdarima) (3.2.0)\n",
            "Requirement already satisfied: patsy>=0.5.4 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.13.2->pmdarima) (0.5.6)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.4->statsmodels>=0.13.2->pmdarima) (1.16.0)\n",
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = read_csv(\"/content/drive/My Drive/airline-passengers.csv\", header=0, index_col=0, parse_dates=True)\n",
        "series = df['Passengers']\n",
        "\n",
        "df.head()"
      ],
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          "height": 238
        },
        "id": "TP8mlUNCbMFk",
        "outputId": "570e89cc-893b-4637-9e44-f8ee06fcc1c3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Passengers\n",
              "Month                 \n",
              "1949-01-01         112\n",
              "1949-02-01         118\n",
              "1949-03-01         132\n",
              "1949-04-01         129\n",
              "1949-05-01         121"
            ],
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              "      <th>Passengers</th>\n",
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              "      <th>1949-01-01</th>\n",
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          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_H6GdDTfdeoI",
        "outputId": "bb9e9f78-b489-42dd-b1e4-9a2b034789a5"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 144 entries, 1949-01-01 to 1960-12-01\n",
            "Data columns (total 1 columns):\n",
            " #   Column      Non-Null Count  Dtype\n",
            "---  ------      --------------  -----\n",
            " 0   Passengers  144 non-null    int64\n",
            "dtypes: int64(1)\n",
            "memory usage: 2.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "series.plot()"
      ],
      "metadata": {
        "id": "lOxgiqOMdh66",
        "outputId": "2e3590bd-a349-489f-b66d-580f605c9568",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Month'>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split dataset\n",
        "\n",
        "train, test = series[:140], series[140:]\n",
        "n = len(series)\n",
        "n_train = len(train)\n",
        "n_test = len(test)\n",
        "\n",
        "# plot ilustrasi perbandingan train dan test\n",
        "fig, ax = pyplot.subplots()\n",
        "train.plot(ax=ax)\n",
        "test.plot(ax=ax)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "uUXjTUhOAvnm",
        "outputId": "9968f1ce-d16b-411d-c907-ab9e68982751"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Month'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "re0PtSCyBuVl",
        "outputId": "6d3c580a-6bd3-4fd9-c9f5-a95055ae253d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "1960-04-01    461\n",
              "1960-05-01    472\n",
              "1960-06-01    535\n",
              "1960-07-01    622\n",
              "1960-08-01    606\n",
              "Name: Passengers, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMj956t-B6mY",
        "outputId": "05d7edfc-8940-4d7f-d3ac-f958f5348145"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "1960-09-01    508\n",
              "1960-10-01    461\n",
              "1960-11-01    390\n",
              "1960-12-01    432\n",
              "Name: Passengers, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create model\n",
        "\n",
        "import pmdarima as pm\n",
        "\n",
        "def arimamodel(timeseries):\n",
        "  automodel = pm.auto_arima(timeseries,\n",
        "                            start_p=2,\n",
        "                            start_q=0,\n",
        "                            test=\"adf\",\n",
        "                            seasonal=True,\n",
        "                            trace=True,\n",
        "                            stepwise=False)\n",
        "  return automodel"
      ],
      "metadata": {
        "id": "ciFU55nwB-Bx"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "automodel = arimamodel(train)\n",
        "print(automodel.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Gb2euArCk5F",
        "outputId": "702eb706-87b7-4bb8-a8c0-ae38242a97fb"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " ARIMA(0,0,0)(0,0,0)[1] intercept   : AIC=1736.080, Time=0.02 sec\n",
            " ARIMA(0,0,1)(0,0,0)[1] intercept   : AIC=1568.193, Time=0.07 sec\n",
            " ARIMA(0,0,2)(0,0,0)[1] intercept   : AIC=inf, Time=0.14 sec\n",
            " ARIMA(0,0,3)(0,0,0)[1] intercept   : AIC=inf, Time=0.16 sec\n",
            " ARIMA(0,0,4)(0,0,0)[1] intercept   : AIC=inf, Time=0.39 sec\n",
            " ARIMA(0,0,5)(0,0,0)[1] intercept   : AIC=inf, Time=0.48 sec\n",
            " ARIMA(1,0,0)(0,0,0)[1] intercept   : AIC=1377.877, Time=0.05 sec\n",
            " ARIMA(1,0,1)(0,0,0)[1] intercept   : AIC=1360.503, Time=0.11 sec\n",
            " ARIMA(1,0,2)(0,0,0)[1] intercept   : AIC=1359.761, Time=0.15 sec\n",
            " ARIMA(1,0,3)(0,0,0)[1] intercept   : AIC=1359.892, Time=0.26 sec\n",
            " ARIMA(1,0,4)(0,0,0)[1] intercept   : AIC=1340.424, Time=0.28 sec\n",
            " ARIMA(2,0,0)(0,0,0)[1] intercept   : AIC=1364.799, Time=0.10 sec\n",
            " ARIMA(2,0,1)(0,0,0)[1] intercept   : AIC=1354.208, Time=0.17 sec\n",
            " ARIMA(2,0,2)(0,0,0)[1] intercept   : AIC=1354.391, Time=0.25 sec\n",
            " ARIMA(2,0,3)(0,0,0)[1] intercept   : AIC=1355.518, Time=0.36 sec\n",
            " ARIMA(3,0,0)(0,0,0)[1] intercept   : AIC=1361.978, Time=0.14 sec\n",
            " ARIMA(3,0,1)(0,0,0)[1] intercept   : AIC=1354.261, Time=0.19 sec\n",
            " ARIMA(3,0,2)(0,0,0)[1] intercept   : AIC=inf, Time=0.32 sec\n",
            " ARIMA(4,0,0)(0,0,0)[1] intercept   : AIC=1362.976, Time=0.21 sec\n",
            " ARIMA(4,0,1)(0,0,0)[1] intercept   : AIC=1355.944, Time=0.28 sec\n",
            " ARIMA(5,0,0)(0,0,0)[1] intercept   : AIC=1358.871, Time=0.24 sec\n",
            "\n",
            "Best model:  ARIMA(1,0,4)(0,0,0)[1] intercept\n",
            "Total fit time: 4.387 seconds\n",
            "                               SARIMAX Results                                \n",
            "==============================================================================\n",
            "Dep. Variable:                      y   No. Observations:                  140\n",
            "Model:               SARIMAX(1, 0, 4)   Log Likelihood                -663.212\n",
            "Date:                Wed, 17 Jan 2024   AIC                           1340.424\n",
            "Time:                        15:47:23   BIC                           1361.016\n",
            "Sample:                    01-01-1949   HQIC                          1348.792\n",
            "                         - 08-01-1960                                         \n",
            "Covariance Type:                  opg                                         \n",
            "==============================================================================\n",
            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "intercept      0.9429      4.160      0.227      0.821      -7.210       9.096\n",
            "ar.L1          0.9969      0.014     69.668      0.000       0.969       1.025\n",
            "ma.L1          0.3733      0.075      4.974      0.000       0.226       0.520\n",
            "ma.L2         -0.2165      0.106     -2.036      0.042      -0.425      -0.008\n",
            "ma.L3         -0.2114      0.080     -2.658      0.008      -0.367      -0.056\n",
            "ma.L4         -0.4538      0.102     -4.454      0.000      -0.653      -0.254\n",
            "sigma2       734.3013     89.957      8.163      0.000     557.989     910.613\n",
            "===================================================================================\n",
            "Ljung-Box (L1) (Q):                   0.31   Jarque-Bera (JB):                 4.88\n",
            "Prob(Q):                              0.58   Prob(JB):                         0.09\n",
            "Heteroskedasticity (H):               6.40   Skew:                             0.44\n",
            "Prob(H) (two-sided):                  0.00   Kurtosis:                         3.26\n",
            "===================================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# forecast\n",
        "\n",
        "predictions_arima = automodel.predict(n_periods=test.shape[0])\n",
        "predictions_arima"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yMQzz0zUDKQk",
        "outputId": "604745fb-6460-434a-b07e-d9487ac6cea7"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1960-09-01    575.068635\n",
              "1960-10-01    525.285415\n",
              "1960-11-01    492.800914\n",
              "1960-12-01    496.965886\n",
              "Freq: MS, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyplot.figure(figsize=(12,7))\n",
        "pyplot.plot(test.index, test, label='Actual')\n",
        "pyplot.plot(test.index, predictions_arima, label='Predicted')\n",
        "pyplot.title('Forecasting - ARIMA')\n",
        "pyplot.xlabel('Tanggal')\n",
        "pyplot.ylabel('Value')\n",
        "pyplot.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "sd_EHHTKDo3m",
        "outputId": "f4355611-39f4-4dc1-f1d4-e38e8aa8f673"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ebd908a81f0>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate\n",
        "\n",
        "rmse = sqrt(mean_squared_error(test, predictions_arima))\n",
        "print('Test RMSE: %.3f' % rmse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3dAEdoHEn9U",
        "outputId": "4ad2e4af-9bb8-4661-d9e1-5e519c352db5"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test RMSE: 76.517\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}