Stage_E_Time_Series.ipynb

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      "name": "Stage E Time Series.ipynb",
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          "text": [
            "Requirement already satisfied: fbprophet in /usr/local/lib/python3.7/dist-packages (0.7.1)\n",
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            "Requirement already satisfied: pystan>=2.14 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.19.1.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.21.5)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.3.5)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (3.2.2)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.0.9)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.4.0)\n",
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            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->fbprophet) (2018.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (1.4.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (3.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (0.11.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.0.0->fbprophet) (3.10.0.2)\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm\n",
        "from pylab import rcParams\n",
        "import statsmodels.api as sm\n",
        "from sklearn.metrics import mean_squared_error\n",
        "!pip install fbprophet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/time series.csv\")"
      ],
      "metadata": {
        "id": "T-2raGXGtK_V"
      },
      "execution_count": 78,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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          "height": 206
        },
        "id": "7-Tmmryesotq",
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      "outputs": [
        {
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              "              FullDate  Tmax   SysLoad  GasPrice  ElecPrice\n",
              "0  2010-09-01 00:00:00  15.8  1688.215      3.69     23.710\n",
              "1  2010-09-01 01:00:00  15.8  1669.195      3.69     22.635\n",
              "2  2010-09-01 02:00:00  15.8  1491.980      3.69     22.565\n",
              "3  2010-09-01 03:00:00  15.8  1330.760      3.69     18.910\n",
              "4  2010-09-01 04:00:00  15.8  1247.940      3.69     18.030"
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        "df.info()"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 92016 entries, 0 to 92015\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   FullDate   92016 non-null  object \n",
            " 1   Tmax       92016 non-null  float64\n",
            " 2   SysLoad    92016 non-null  float64\n",
            " 3   GasPrice   92016 non-null  float64\n",
            " 4   ElecPrice  92016 non-null  float64\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 3.5+ MB\n"
          ]
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      "source": [
        "df.isnull().sum()"
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        "outputId": "4298c34d-d791-4cc7-95b4-2a2f49ab118e"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FullDate     0\n",
              "Tmax         0\n",
              "SysLoad      0\n",
              "GasPrice     0\n",
              "ElecPrice    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
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        "df.mean()"
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      },
      "execution_count": 85,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
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              "Tmax         231.696688\n",
              "SysLoad      797.623357\n",
              "GasPrice       6.016163\n",
              "ElecPrice     65.798356\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
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      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(92016, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 86
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      },
      "execution_count": 87,
      "outputs": [
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          "data": {
            "text/plain": [
              "               Tmax       SysLoad      GasPrice     ElecPrice\n",
              "count  92016.000000  92016.000000  92016.000000  92016.000000\n",
              "mean     231.696688    797.623357      6.016163     65.798356\n",
              "std     4562.128748    613.520156      2.608116    213.144084\n",
              "min        9.900000  -1596.810100      0.000000   -924.820000\n",
              "25%       17.300000    417.119800      3.936900     31.543750\n",
              "50%       21.900000    856.467925      5.165600     48.715000\n",
              "75%       27.600000   1221.293675      8.023300     76.495000\n",
              "max    99999.900000   3264.166300     29.990000  14700.000000"
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              "      <td>92016.000000</td>\n",
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              "      <td>92016.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>231.696688</td>\n",
              "      <td>797.623357</td>\n",
              "      <td>6.016163</td>\n",
              "      <td>65.798356</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4562.128748</td>\n",
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              "      <td>213.144084</td>\n",
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              "      <th>min</th>\n",
              "      <td>9.900000</td>\n",
              "      <td>-1596.810100</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-924.820000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.300000</td>\n",
              "      <td>417.119800</td>\n",
              "      <td>3.936900</td>\n",
              "      <td>31.543750</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>21.900000</td>\n",
              "      <td>856.467925</td>\n",
              "      <td>5.165600</td>\n",
              "      <td>48.715000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>27.600000</td>\n",
              "      <td>1221.293675</td>\n",
              "      <td>8.023300</td>\n",
              "      <td>76.495000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>99999.900000</td>\n",
              "      <td>3264.166300</td>\n",
              "      <td>29.990000</td>\n",
              "      <td>14700.000000</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6Yoybt66uMXS",
        "outputId": "9d2c4f91-2b73-45fd-99ad-37fbfea4b3fd"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  FullDate  Tmax    SysLoad  GasPrice  ElecPrice\n",
              "92011  2021-02-28 19:00:00  24.3  449.63625    5.4848    117.925\n",
              "92012  2021-02-28 20:00:00  24.3  815.59975    5.4848     87.880\n",
              "92013  2021-02-28 21:00:00  24.3  745.12325    5.4848     62.060\n",
              "92014  2021-02-28 22:00:00  24.3  694.95150    5.4848     62.000\n",
              "92015  2021-02-28 23:00:00  24.3  714.69630    5.4848     62.955"
            ],
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              "      <th>FullDate</th>\n",
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              "      <th>GasPrice</th>\n",
              "      <th>ElecPrice</th>\n",
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              "      <th>92011</th>\n",
              "      <td>2021-02-28 19:00:00</td>\n",
              "      <td>24.3</td>\n",
              "      <td>449.63625</td>\n",
              "      <td>5.4848</td>\n",
              "      <td>117.925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92012</th>\n",
              "      <td>2021-02-28 20:00:00</td>\n",
              "      <td>24.3</td>\n",
              "      <td>815.59975</td>\n",
              "      <td>5.4848</td>\n",
              "      <td>87.880</td>\n",
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              "    <tr>\n",
              "      <th>92013</th>\n",
              "      <td>2021-02-28 21:00:00</td>\n",
              "      <td>24.3</td>\n",
              "      <td>745.12325</td>\n",
              "      <td>5.4848</td>\n",
              "      <td>62.060</td>\n",
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              "    <tr>\n",
              "      <th>92014</th>\n",
              "      <td>2021-02-28 22:00:00</td>\n",
              "      <td>24.3</td>\n",
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              "      <td>5.4848</td>\n",
              "      <td>62.000</td>\n",
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              "    <tr>\n",
              "      <th>92015</th>\n",
              "      <td>2021-02-28 23:00:00</td>\n",
              "      <td>24.3</td>\n",
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              "      <td>5.4848</td>\n",
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-7a3ac399-d7bb-4ea3-8fe2-5ff5424035a9');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting FullDate to date time\n",
        "df['FullDate'] = pd.to_datetime(df['FullDate'])"
      ],
      "metadata": {
        "id": "xCXaE8pD_lRr"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#set FullDate as index so i can resample \n",
        "df.set_index('FullDate',inplace=True)"
      ],
      "metadata": {
        "id": "Ykg9Jb02SLeL"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Exploring the time series using decompose\n",
        "rcParams['figure.figsize'] = 15, 8\n",
        "decompose_series = sm.tsa.seasonal_decompose(df['ElecPrice'], model='additive')\n",
        "decompose_series.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "_bEs3ksFSS-7",
        "outputId": "7852c93d-f278-48ce-bc73-dbc07549d351"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#resampling\n",
        "df_daily = df.resample('D').sum()\n"
      ],
      "metadata": {
        "id": "YBZxZmKgS5gw"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_daily.head()\n"
      ],
      "metadata": {
        "id": "JRiGn8fWS_83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "0b765755-9f02-4ae7-8a80-dfe523248120"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Tmax    SysLoad  GasPrice  ElecPrice\n",
              "FullDate                                         \n",
              "2010-09-01  379.2  41565.380     88.56   2751.700\n",
              "2010-09-02  369.6  38898.990     88.56    570.810\n",
              "2010-09-03  439.2  41665.760    106.80    504.975\n",
              "2010-09-04  328.8  35638.085     81.36    502.275\n",
              "2010-09-05  357.6  35611.410    106.80    500.555"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Tmax</th>\n",
              "      <th>SysLoad</th>\n",
              "      <th>GasPrice</th>\n",
              "      <th>ElecPrice</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FullDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-09-01</th>\n",
              "      <td>379.2</td>\n",
              "      <td>41565.380</td>\n",
              "      <td>88.56</td>\n",
              "      <td>2751.700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-09-02</th>\n",
              "      <td>369.6</td>\n",
              "      <td>38898.990</td>\n",
              "      <td>88.56</td>\n",
              "      <td>570.810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-09-03</th>\n",
              "      <td>439.2</td>\n",
              "      <td>41665.760</td>\n",
              "      <td>106.80</td>\n",
              "      <td>504.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-09-04</th>\n",
              "      <td>328.8</td>\n",
              "      <td>35638.085</td>\n",
              "      <td>81.36</td>\n",
              "      <td>502.275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-09-05</th>\n",
              "      <td>357.6</td>\n",
              "      <td>35611.410</td>\n",
              "      <td>106.80</td>\n",
              "      <td>500.555</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dab37687-7c47-482b-b111-83eef3030025');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting into train and test\n",
        "train_df = df_daily[:2757]\n",
        "test_df = df_daily[2757:]"
      ],
      "metadata": {
        "id": "-f1oyJdQYbwa"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from fbprophet import Prophet"
      ],
      "metadata": {
        "id": "tcljpeswTPcD"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Model building and fitting data\n",
        "model = Prophet()\n"
      ],
      "metadata": {
        "id": "v6P8X0pgAa7f"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "Gew7LRY3Uhq_"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_df.reset_index(inplace=True)"
      ],
      "metadata": {
        "id": "Sgc0PxtxU6Zx"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#resetting index so i can have the FullDate as columns\n",
        "train_df.reset_index(inplace=True)\n",
        "test_df.reset_index(inplace=True)\n",
        "\n",
        "train_df = train_df.rename(columns={\"FullDate\": \"ds\", \"ElecPrice\": \"y\"})\n",
        "train_df = train_df[['ds', 'y']]\n",
        "train_df.head()\n",
        "\n",
        "univar_forecast[['ds','yhat','yhat_lower','yhat_upper']].tail()\n",
        "\n",
        "\n",
        "\n",
        "test_df = test_df.rename(columns={\"FullDate\": \"ds\", \"ElecPrice\": \"y\"})\n",
        "test_df = test_df[['ds', 'y']]\n",
        "test_df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Y30_W_MNY6hb",
        "outputId": "6bfa9362-9dc0-4b27-f13c-0454afb854f8"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds        y\n",
              "0 2018-03-20  1833.00\n",
              "1 2018-03-21  1730.49\n",
              "2 2018-03-22  2260.55\n",
              "3 2018-03-23  2138.76\n",
              "4 2018-03-24  1721.70"
            ],
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              "      <td>2018-03-21</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-03-22</td>\n",
              "      <td>2260.55</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-03-23</td>\n",
              "      <td>2138.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2018-03-24</td>\n",
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              "</table>\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-60bf1129-555a-4e77-8a60-b1aee6218a55');\n",
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              "            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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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_df)\n",
        "train_df = train_df.rename(columns={\"FullDate\": \"ds\", \"ElecPrice\": \"y\"})\n",
        "train_df = train_df[['ds', 'y']]"
      ],
      "metadata": {
        "id": "EeSETE_tT698",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9b14a87-7724-456c-a608-bae157e14b6c"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Prediction\n",
        "uni_var_future = model.make_future_dataframe(periods=1077, freq='D') \n",
        "univar_forecast = model.predict(uni_var_future)\n",
        "\n",
        "\n",
        "\n",
        "univar_forecast[['ds','yhat','yhat_lower','yhat_upper']].tail()\n",
        "\n",
        "test_df = test_df.rename(columns={\"FullDate\": \"ds\", \"ElecPrice\": \"y\"})\n",
        "test_df = test_df[['ds', 'y']]\n",
        "test_df.head()"
      ],
      "metadata": {
        "id": "BrgRQraUAK5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5daf3483-e2bb-4841-e502-9677e7d2225f"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          ds        y\n",
              "0 2018-03-20  1833.00\n",
              "1 2018-03-21  1730.49\n",
              "2 2018-03-22  2260.55\n",
              "3 2018-03-23  2138.76\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-03-21</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2018-03-22</td>\n",
              "      <td>2260.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-03-23</td>\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",
              "      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-819ef8be-9e9a-4c0c-963e-92a584b171da 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-819ef8be-9e9a-4c0c-963e-92a584b171da');\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": 103
        }
      ]
    }
  ]
}