SARIMAX完成.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Ken-huea/Time-series-analysis/blob/main/SARIMAX%E5%AE%8C%E6%88%90.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_oH0zxHXS_Rr"
      },
      "source": [
        "## 時系列モデルの種類\n",
        "- AR（自己回帰モデル）　　現在の値は、過去の値から決まる\n",
        "- MA（移動平均モデル）　　現在の値は、過去の「実績と予測のズレ」から決まる\n",
        "- ARMA（自己回帰 移動平均モデル） ...AR + MA\n",
        "- ARIMA（自己回帰 和分 移動平均モデル）  ...ARMA + I(時間帯によって平均値はブレる)\n",
        "- SARIMA（季節 自己回帰 和分 移動平均モデル） ...S(データに周期性がある) + ARIMA\n",
        "- SARIMAX ...SARIMA + 説明変数"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### データ読込と分割"
      ],
      "metadata": {
        "id": "4DR2v5p9dMEC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#いろいろインポート\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm \n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "#データ読み込み\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/時系列解析/Qiita用/☆１/SARIMAXの実装.csv\",index_col=\"年月日\",encoding=\"shift_jis\",parse_dates=True)\n",
        "\n",
        "#dfのgroupby\n",
        "df = df.resample(\"M\").mean()\n",
        "print(df.shape)\n",
        "df"
      ],
      "metadata": {
        "id": "SfbzxjRDY3Gd",
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          "base_uri": "https://localhost:8080/",
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        "outputId": "783eab8e-6fa3-48c5-eac2-b3c1f28c5b41"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n",
            "(120, 3)\n"
          ]
        },
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              "              平均気温(℃)  降水量の合計(mm)  日照時間(時間)\n",
              "年月日                                        \n",
              "2010-01-31   7.022581    0.290323  7.158065\n",
              "2010-02-28   6.500000    4.107143  4.225000\n",
              "2010-03-31   9.096774    4.629032  4.509677\n",
              "2010-04-30  12.393333    7.133333  4.663333\n",
              "2010-05-31  18.951613    3.677419  6.412903\n",
              "...               ...         ...       ...\n",
              "2019-08-31  28.396774    3.548387  6.058065\n",
              "2019-09-30  25.060000    6.566667  4.586667\n",
              "2019-10-31  19.425806   17.080645  3.638710\n",
              "2019-11-30  13.100000    5.216667  5.676667\n",
              "2019-12-31   8.474194    2.467742  4.148387\n",
              "\n",
              "[120 rows x 3 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>平均気温(℃)</th>\n",
              "      <th>降水量の合計(mm)</th>\n",
              "      <th>日照時間(時間)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>年月日</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-31</th>\n",
              "      <td>7.022581</td>\n",
              "      <td>0.290323</td>\n",
              "      <td>7.158065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-28</th>\n",
              "      <td>6.500000</td>\n",
              "      <td>4.107143</td>\n",
              "      <td>4.225000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-03-31</th>\n",
              "      <td>9.096774</td>\n",
              "      <td>4.629032</td>\n",
              "      <td>4.509677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-04-30</th>\n",
              "      <td>12.393333</td>\n",
              "      <td>7.133333</td>\n",
              "      <td>4.663333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-05-31</th>\n",
              "      <td>18.951613</td>\n",
              "      <td>3.677419</td>\n",
              "      <td>6.412903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>2019-08-31</th>\n",
              "      <td>28.396774</td>\n",
              "      <td>3.548387</td>\n",
              "      <td>6.058065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
              "      <td>25.060000</td>\n",
              "      <td>6.566667</td>\n",
              "      <td>4.586667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-31</th>\n",
              "      <td>19.425806</td>\n",
              "      <td>17.080645</td>\n",
              "      <td>3.638710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>13.100000</td>\n",
              "      <td>5.216667</td>\n",
              "      <td>5.676667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>8.474194</td>\n",
              "      <td>2.467742</td>\n",
              "      <td>4.148387</td>\n",
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              "  </tbody>\n",
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              "<p>120 rows × 3 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 1
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "分割"
      ],
      "metadata": {
        "id": "JDqj7jsXdOXf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fVDkNL2qS_R5",
        "outputId": "24772e4a-73d4-4bc0-b525-683a8ca8cb55"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:6: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "#　月ごとに集計したデータも準備しておく\n",
        "df_train = df[\"2010\":\"2018\"] # df_month\n",
        "\n",
        "df = df[\"2010\":\"2019\"] # df_month_all\n",
        "\n",
        "df_test = df[\"2019\"]   # df_month_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 学習"
      ],
      "metadata": {
        "id": "B-hLOpbWdSkr"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "_Z6ouFrYS_R6",
        "outputId": "e786e2aa-0fef-4abc-bc35-5876d41c7cd2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                     SARIMAX Results                                      \n",
              "==========================================================================================\n",
              "Dep. Variable:                            平均気温(℃)   No. Observations:                  108\n",
              "Model:             SARIMAX(1, 1, 1)x(0, 1, 1, 12)   Log Likelihood                -141.757\n",
              "Date:                            Wed, 14 Sep 2022   AIC                            295.514\n",
              "Time:                                    05:12:55   BIC                            310.837\n",
              "Sample:                                01-31-2010   HQIC                           301.705\n",
              "                                     - 12-31-2018                                         \n",
              "Covariance Type:                              opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "降水量の合計(mm)    -0.0054      0.036     -0.147      0.883      -0.077       0.066\n",
              "日照時間(時間)       0.3838      0.154      2.488      0.013       0.081       0.686\n",
              "ar.L1          0.2493      0.119      2.103      0.035       0.017       0.482\n",
              "ma.L1         -0.9988      2.105     -0.474      0.635      -5.125       3.127\n",
              "ma.S.L12      -0.9971     10.902     -0.091      0.927     -22.364      20.370\n",
              "sigma2         0.8261      9.761      0.085      0.933     -18.306      19.958\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):                 1.97\n",
              "Prob(Q):                              0.79   Prob(JB):                         0.37\n",
              "Heteroskedasticity (H):               0.89   Skew:                             0.31\n",
              "Prob(H) (two-sided):                  0.74   Kurtosis:                         3.35\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>平均気温(℃)</td>            <th>  No. Observations:  </th>    <td>108</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)x(0, 1, 1, 12)</td> <th>  Log Likelihood     </th> <td>-141.757</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Wed, 14 Sep 2022</td>        <th>  AIC                </th>  <td>295.514</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>05:12:55</td>            <th>  BIC                </th>  <td>310.837</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                    <td>01-31-2010</td>           <th>  HQIC               </th>  <td>301.705</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                          <td>- 12-31-2018</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>降水量の合計(mm)</th> <td>   -0.0054</td> <td>    0.036</td> <td>   -0.147</td> <td> 0.883</td> <td>   -0.077</td> <td>    0.066</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>日照時間(時間)</th>   <td>    0.3838</td> <td>    0.154</td> <td>    2.488</td> <td> 0.013</td> <td>    0.081</td> <td>    0.686</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>      <td>    0.2493</td> <td>    0.119</td> <td>    2.103</td> <td> 0.035</td> <td>    0.017</td> <td>    0.482</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>      <td>   -0.9988</td> <td>    2.105</td> <td>   -0.474</td> <td> 0.635</td> <td>   -5.125</td> <td>    3.127</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.S.L12</th>   <td>   -0.9971</td> <td>   10.902</td> <td>   -0.091</td> <td> 0.927</td> <td>  -22.364</td> <td>   20.370</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>     <td>    0.8261</td> <td>    9.761</td> <td>    0.085</td> <td> 0.933</td> <td>  -18.306</td> <td>   19.958</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.07</td> <th>  Jarque-Bera (JB):  </th> <td>1.97</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.79</td> <th>  Prob(JB):          </th> <td>0.37</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>0.89</td> <th>  Skew:              </th> <td>0.31</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.74</td> <th>  Kurtosis:          </th> <td>3.35</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
        "\n",
        "y = df_train[\"平均気温(℃)\"]\n",
        "X = df_train[[\"降水量の合計(mm)\",\"日照時間(時間)\"]]\n",
        "sarima_model = sm.tsa.SARIMAX(y,X, order=(1,1,1),seasonal_order=(0,1,1,12))\n",
        "result = sarima_model.fit()\n",
        "\n",
        "result.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LsL42YkIS_R7"
      },
      "source": [
        "### グリッドサーチ"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vfHNkyB1S_R7"
      },
      "outputs": [],
      "source": [
        "import itertools\n",
        "\n",
        "p = q = range(0, 3)\n",
        "sp = sd = sq = range(0, 2)\n",
        "\n",
        "#p,q,sp,sd,sqの組み合わせのリストを作成する。和分=1,周期=12は固定\n",
        "pdq = [(x[0], 1, x[1]) for x in list(itertools.product(p, q))]\n",
        "seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(sp, sd, sq))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifz2RE9yS_R8",
        "outputId": "abab5d9a-105a-452b-f43c-94cefe4267df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARIMAパラメータ(0, 1, 0), 季節変動パラメータ(0, 0, 0, 12) - AIC: 613.9551631312182\n",
            "ARIMAパラメータ(0, 1, 0), 季節変動パラメータ(0, 0, 1, 12) - AIC: 528.8431011738005\n",
            "ARIMAパラメータ(0, 1, 0), 季節変動パラメータ(0, 1, 0, 12) - AIC: 359.2434101619832\n",
            "ARIMAパラメータ(0, 1, 0), 季節変動パラメータ(0, 1, 1, 12) - AIC: 333.37390920876607\n",
            "ARIMAパラメータ(0, 1, 0), 季節変動パラメータ(1, 0, 0, 12) - AIC: 427.0478625567827\n",
            "ARIMAパラメータ(0, 1, 0), 季節変動パラメータ(1, 0, 1, 12) - AIC: 403.0842838714033\n",
            "ARIMAパラメータ(0, 1, 0), 季節変動パラメータ(1, 1, 0, 12) - AIC: 351.68990322961935\n",
            "ARIMAパラメータ(0, 1, 0), 季節変動パラメータ(1, 1, 1, 12) - AIC: 334.5500870402405\n",
            "ARIMAパラメータ(0, 1, 1), 季節変動パラメータ(0, 0, 0, 12) - AIC: 560.8256802210583\n",
            "ARIMAパラメータ(0, 1, 1), 季節変動パラメータ(0, 0, 1, 12) - AIC: 505.9826928198994\n",
            "ARIMAパラメータ(0, 1, 1), 季節変動パラメータ(0, 1, 0, 12) - AIC: 321.08895893648213\n",
            "ARIMAパラメータ(0, 1, 1), 季節変動パラメータ(0, 1, 1, 12) - AIC: 306.8216431466753\n",
            "ARIMAパラメータ(0, 1, 1), 季節変動パラメータ(1, 0, 0, 12) - AIC: 402.1304495137347\n",
            "ARIMAパラメータ(0, 1, 1), 季節変動パラメータ(1, 0, 1, 12) - AIC: 387.9467990151249\n",
            "ARIMAパラメータ(0, 1, 1), 季節変動パラメータ(1, 1, 0, 12) - AIC: 315.48237396029674\n",
            "ARIMAパラメータ(0, 1, 1), 季節変動パラメータ(1, 1, 1, 12) - AIC: 308.0869292768329\n",
            "ARIMAパラメータ(0, 1, 2), 季節変動パラメータ(0, 0, 0, 12) - AIC: 533.2988530629615\n",
            "ARIMAパラメータ(0, 1, 2), 季節変動パラメータ(0, 0, 1, 12) - AIC: 485.1632808465877\n",
            "ARIMAパラメータ(0, 1, 2), 季節変動パラメータ(0, 1, 0, 12) - AIC: 321.31662384705646\n",
            "ARIMAパラメータ(0, 1, 2), 季節変動パラメータ(0, 1, 1, 12) - AIC: 302.96342436529414\n",
            "ARIMAパラメータ(0, 1, 2), 季節変動パラメータ(1, 0, 0, 12) - AIC: 396.4805423519526\n",
            "ARIMAパラメータ(0, 1, 2), 季節変動パラメータ(1, 0, 1, 12) - AIC: 378.04649691014134\n",
            "ARIMAパラメータ(0, 1, 2), 季節変動パラメータ(1, 1, 0, 12) - AIC: 314.2499607594593\n",
            "ARIMAパラメータ(0, 1, 2), 季節変動パラメータ(1, 1, 1, 12) - AIC: 304.65194347682274\n",
            "ARIMAパラメータ(1, 1, 0), 季節変動パラメータ(0, 0, 0, 12) - AIC: 533.5447846233246\n",
            "ARIMAパラメータ(1, 1, 0), 季節変動パラメータ(0, 0, 1, 12) - AIC: 490.6907578438695\n",
            "ARIMAパラメータ(1, 1, 0), 季節変動パラメータ(0, 1, 0, 12) - AIC: 343.5851964711973\n",
            "ARIMAパラメータ(1, 1, 0), 季節変動パラメータ(0, 1, 1, 12) - AIC: 323.7125322123366\n",
            "ARIMAパラメータ(1, 1, 0), 季節変動パラメータ(1, 0, 0, 12) - AIC: 419.3353604841778\n",
            "ARIMAパラメータ(1, 1, 0), 季節変動パラメータ(1, 0, 1, 12) - AIC: 399.0365826723957\n",
            "ARIMAパラメータ(1, 1, 0), 季節変動パラメータ(1, 1, 0, 12) - AIC: 337.7745081494727\n",
            "ARIMAパラメータ(1, 1, 0), 季節変動パラメータ(1, 1, 1, 12) - AIC: 324.7850478764607\n",
            "ARIMAパラメータ(1, 1, 1), 季節変動パラメータ(0, 0, 0, 12) - AIC: 532.1564218066823\n",
            "ARIMAパラメータ(1, 1, 1), 季節変動パラメータ(0, 0, 1, 12) - AIC: 492.68621050961895\n",
            "ARIMAパラメータ(1, 1, 1), 季節変動パラメータ(0, 1, 0, 12) - AIC: 320.72265710691636\n",
            "ARIMAパラメータ(1, 1, 1), 季節変動パラメータ(0, 1, 1, 12) - AIC: 302.0302525908307\n",
            "ARIMAパラメータ(1, 1, 1), 季節変動パラメータ(1, 0, 0, 12) - AIC: 393.91117950282194\n",
            "ARIMAパラメータ(1, 1, 1), 季節変動パラメータ(1, 0, 1, 12) - AIC: 373.00448913249\n",
            "ARIMAパラメータ(1, 1, 1), 季節変動パラメータ(1, 1, 0, 12) - AIC: 313.813547439821\n",
            "ARIMAパラメータ(1, 1, 1), 季節変動パラメータ(1, 1, 1, 12) - AIC: 303.5376230748421\n",
            "ARIMAパラメータ(1, 1, 2), 季節変動パラメータ(0, 0, 0, 12) - AIC: 556.8465412176423\n",
            "ARIMAパラメータ(1, 1, 2), 季節変動パラメータ(0, 0, 1, 12) - AIC: 498.3652323699792\n",
            "ARIMAパラメータ(1, 1, 2), 季節変動パラメータ(0, 1, 0, 12) - AIC: 322.6820935239404\n",
            "ARIMAパラメータ(1, 1, 2), 季節変動パラメータ(0, 1, 1, 12) - AIC: 304.0300978940268\n",
            "ARIMAパラメータ(1, 1, 2), 季節変動パラメータ(1, 0, 0, 12) - AIC: 395.7864366492004\n",
            "ARIMAパラメータ(1, 1, 2), 季節変動パラメータ(1, 0, 1, 12) - AIC: 376.0395961131066\n",
            "ARIMAパラメータ(1, 1, 2), 季節変動パラメータ(1, 1, 0, 12) - AIC: 315.81359423358504\n",
            "ARIMAパラメータ(1, 1, 2), 季節変動パラメータ(1, 1, 1, 12) - AIC: 305.19199732229777\n",
            "ARIMAパラメータ(2, 1, 0), 季節変動パラメータ(0, 0, 0, 12) - AIC: 528.0400726209923\n",
            "ARIMAパラメータ(2, 1, 0), 季節変動パラメータ(0, 0, 1, 12) - AIC: 492.67925689845623\n",
            "ARIMAパラメータ(2, 1, 0), 季節変動パラメータ(0, 1, 0, 12) - AIC: 342.6662020975275\n",
            "ARIMAパラメータ(2, 1, 0), 季節変動パラメータ(0, 1, 1, 12) - AIC: 323.8807684830653\n",
            "ARIMAパラメータ(2, 1, 0), 季節変動パラメータ(1, 0, 0, 12) - AIC: 420.7707093555785\n",
            "ARIMAパラメータ(2, 1, 0), 季節変動パラメータ(1, 0, 1, 12) - AIC: 400.8442999749503\n",
            "ARIMAパラメータ(2, 1, 0), 季節変動パラメータ(1, 1, 0, 12) - AIC: 336.60772596759745\n",
            "ARIMAパラメータ(2, 1, 0), 季節変動パラメータ(1, 1, 1, 12) - AIC: 324.88852522413197\n",
            "ARIMAパラメータ(2, 1, 1), 季節変動パラメータ(0, 0, 0, 12) - AIC: 465.58287223677576\n",
            "ARIMAパラメータ(2, 1, 1), 季節変動パラメータ(0, 0, 1, 12) - AIC: 490.95710326414775\n",
            "ARIMAパラメータ(2, 1, 1), 季節変動パラメータ(0, 1, 0, 12) - AIC: 322.6306961763627\n",
            "ARIMAパラメータ(2, 1, 1), 季節変動パラメータ(0, 1, 1, 12) - AIC: 304.02944168809387\n",
            "ARIMAパラメータ(2, 1, 1), 季節変動パラメータ(1, 0, 0, 12) - AIC: 395.61680999033143\n",
            "ARIMAパラメータ(2, 1, 1), 季節変動パラメータ(1, 0, 1, 12) - AIC: 381.84957899081905\n",
            "ARIMAパラメータ(2, 1, 1), 季節変動パラメータ(1, 1, 0, 12) - AIC: 315.81366861508815\n",
            "ARIMAパラメータ(2, 1, 1), 季節変動パラメータ(1, 1, 1, 12) - AIC: 305.1917968196097\n",
            "ARIMAパラメータ(2, 1, 2), 季節変動パラメータ(0, 0, 0, 12) - AIC: 397.55188293073206\n",
            "ARIMAパラメータ(2, 1, 2), 季節変動パラメータ(0, 0, 1, 12) - AIC: 379.6374809439989\n",
            "ARIMAパラメータ(2, 1, 2), 季節変動パラメータ(0, 1, 0, 12) - AIC: 322.88565150911785\n",
            "ARIMAパラメータ(2, 1, 2), 季節変動パラメータ(0, 1, 1, 12) - AIC: 305.18418794040224\n",
            "ARIMAパラメータ(2, 1, 2), 季節変動パラメータ(1, 0, 0, 12) - AIC: 378.88992171882956\n",
            "ARIMAパラメータ(2, 1, 2), 季節変動パラメータ(1, 0, 1, 12) - AIC: 365.95922649286933\n",
            "ARIMAパラメータ(2, 1, 2), 季節変動パラメータ(1, 1, 0, 12) - AIC: 316.7900411637308\n",
            "ARIMAパラメータ(2, 1, 2), 季節変動パラメータ(1, 1, 1, 12) - AIC: 306.2455195594408\n",
            "AIC最小のモデル： [(1, 1, 1), (0, 1, 1, 12), 302.0302525908307]\n"
          ]
        }
      ],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "best_result = [0, 0, 10000000]\n",
        "for param in pdq:\n",
        "    for param_seasonal in seasonal_pdq:\n",
        "        try:\n",
        "            mod = SARIMAX(y, order = param, seasonal_order = param_seasonal)\n",
        "            results = mod.fit()\n",
        "            print('ARIMAパラメータ{}, 季節変動パラメータ{} - AIC: {}'.format(param, param_seasonal, results.aic))\n",
        "            \n",
        "            if results.aic < best_result[2]:\n",
        "                best_result = [param, param_seasonal, results.aic]\n",
        "        except:\n",
        "            continue\n",
        "\n",
        "print('AIC最小のモデル：', best_result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KesCudYS_R_"
      },
      "source": [
        "### 波形の予測"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "[exogは独立変数（説明変数）](https://it.senatus.jp/statsmodels%E3%81%AEendog%E3%82%84%E3%80%81exog%E3%81%A8%E3%81%AF%E4%BD%95%E3%81%8B%EF%BC%9F/)"
      ],
      "metadata": {
        "id": "Rtbq579wdn1n"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "8AJQwTsjS_SA",
        "outputId": "10a7aff9-40ab-4993-af0a-ee3e6f36e599"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff49dd63c50>]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "bestPred = result.predict('2018-12', '2019-12',exog=df_test[[\"降水量の合計(mm)\",\"日照時間(時間)\"]])\n",
        "plt.plot(df[\"平均気温(℃)\"])\n",
        "plt.plot(bestPred, \"r\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "tvftLOXCS_SA",
        "outputId": "a745e515-448c-44b0-b701-9f379b6247ec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            predicted_mean\n",
              "2018-12-31        7.586499\n",
              "2019-01-31        5.843569\n",
              "2019-02-28        5.834012\n",
              "2019-03-31        9.583526\n",
              "2019-04-30       14.758296\n",
              "2019-05-31       19.846008\n",
              "2019-06-30       22.163614\n",
              "2019-07-31       25.507568\n",
              "2019-08-31       27.799539\n",
              "2019-09-30       23.975673\n",
              "2019-10-31       18.383389\n",
              "2019-11-30       13.379665\n",
              "2019-12-31        7.233408"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>predicted_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-12-31</th>\n",
              "      <td>7.586499</td>\n",
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              "    <tr>\n",
              "      <th>2019-01-31</th>\n",
              "      <td>5.843569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-02-28</th>\n",
              "      <td>5.834012</td>\n",
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              "    <tr>\n",
              "      <th>2019-03-31</th>\n",
              "      <td>9.583526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-30</th>\n",
              "      <td>14.758296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-31</th>\n",
              "      <td>19.846008</td>\n",
              "    </tr>\n",
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              "      <td>27.799539</td>\n",
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              "    <tr>\n",
              "      <th>2019-09-30</th>\n",
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              "    <tr>\n",
              "      <th>2019-11-30</th>\n",
              "      <td>13.379665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-31</th>\n",
              "      <td>7.233408</td>\n",
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              "          const element = document.querySelector('#df-3d73be83-1b99-4a52-80b3-48ad073545d7');\n",
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              "                                                     [key], {});\n",
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              "          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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "bestPred = pd.DataFrame(bestPred)\n",
        "bestPred"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bestPred = bestPred[1:]\n",
        "bestPred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "Y_RUfWRIbzOw",
        "outputId": "8895e4cf-50b1-439e-d861-3d6cb6307a9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            predicted_mean\n",
              "2019-01-31        5.843569\n",
              "2019-02-28        5.834012\n",
              "2019-03-31        9.583526\n",
              "2019-04-30       14.758296\n",
              "2019-05-31       19.846008\n",
              "2019-06-30       22.163614\n",
              "2019-07-31       25.507568\n",
              "2019-08-31       27.799539\n",
              "2019-09-30       23.975673\n",
              "2019-10-31       18.383389\n",
              "2019-11-30       13.379665\n",
              "2019-12-31        7.233408"
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_for_pred = df_test[\"平均気温(℃)\"]\n",
        "test_for_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WMSpGwBTbXgB",
        "outputId": "6781ca09-0dd5-420c-b10d-aa7d13f633fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "年月日\n",
              "2019-01-31     5.596774\n",
              "2019-02-28     7.189286\n",
              "2019-03-31    10.561290\n",
              "2019-04-30    13.640000\n",
              "2019-05-31    19.993548\n",
              "2019-06-30    21.773333\n",
              "2019-07-31    24.070968\n",
              "2019-08-31    28.396774\n",
              "2019-09-30    25.060000\n",
              "2019-10-31    19.425806\n",
              "2019-11-30    13.100000\n",
              "2019-12-31     8.474194\n",
              "Freq: M, Name: 平均気温(℃), dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j7x0wG2MS_SA",
        "outputId": "305ee12e-8c98-4a4a-d8bc-522942d5de88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE 0.9389284544174549\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import mean_squared_error as MSE\n",
        "# y_trainと予測値のRMSEを出す\n",
        "test = df_test[\"平均気温(℃)\"]\n",
        "print(\"RMSE\",np.sqrt(MSE(test_for_pred,bestPred)))"
      ]
    }
  ],
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}