LSTM_DSE_FORECAST_tensorflow_latest.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LSTM_DSE_FORECAST_tensorflow_latest.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.7.6"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3AoKtd0-Q-Fk"
      },
      "source": [
        "# Set The Model Here"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBPnzCcyQ-Fs"
      },
      "source": [
        "def Stock_Forecast(Instrument, TRIAL, PATIENCE, Back):\n",
        "\n",
        "    # check if bdshare is installed if not install\n",
        "    try:\n",
        "      import bdshare\n",
        "    except:\n",
        "      !pip install bdshare\n",
        "    \n",
        "    # importing required libraries\n",
        "    #import matplotlib.pyplot as plt\n",
        "    #%matplotlib inline\n",
        "    import pandas as pd\n",
        "    import numpy as np\n",
        "    import datetime\n",
        "    import tensorflow as tf\n",
        "    import keras\n",
        "    import random as rn\n",
        "    import os\n",
        "    from sklearn.preprocessing import MinMaxScaler\n",
        "    \n",
        "    from tensorflow.keras import backend as K\n",
        "    from tensorflow.keras.models import Sequential, load_model\n",
        "    from tensorflow.keras.layers import LSTM, Dense, RepeatVector, Masking, TimeDistributed\n",
        "    from tensorflow.keras.utils import plot_model\n",
        "    from tensorflow.python.keras.callbacks import ModelCheckpoint\n",
        "    from tensorflow.python.keras.callbacks import EarlyStopping\n",
        "    \n",
        "    from bdshare import get_hist_data\n",
        "    \n",
        "    import plotly.offline as py\n",
        "    import plotly.graph_objs as go\n",
        "    #py.init_notebook_mode(connected=True)\n",
        "    \n",
        "    # stop posting the warning\n",
        "    # os.environ[\"PYTHONHASHSEED\"] = \"0\"\n",
        "    # import tensorflow.python.util.deprecation as deprecation\n",
        "    # deprecation._PRINT_DEPRECATION_WARNINGS = False\n",
        "\n",
        "    # Setting the seed for numpy-generated random numbers\n",
        "    np.random.seed(37)\n",
        "\n",
        "    # Setting the seed for python random numbers\n",
        "    rn.seed(1254)\n",
        "\n",
        "    # Setting the graph-level random seed. (tensorflow 2x)\n",
        "    tf.random.set_seed(89)\n",
        "    \n",
        "    # tensorflow 2x change\n",
        "    session_conf = tf.compat.v1.ConfigProto(\n",
        "        intra_op_parallelism_threads=1, inter_op_parallelism_threads=1\n",
        "    )\n",
        "\n",
        "    # Force Tensorflow to use a single thread (tensorflow 2x)\n",
        "    sess = tf.compat.v1.Session(graph=tf.compat.v1.reset_default_graph(), config=session_conf)\n",
        "    tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=session_conf))\n",
        "\n",
        "    # read the data\n",
        "    #d = pd.read_excel(\"Book3.xlsx\")\n",
        "    to_date = datetime.datetime.today()\n",
        "    from_date = to_date - datetime.timedelta(days=365*2)\n",
        "    df = get_hist_data(from_date,to_date,Instrument)\n",
        "    df = df.reset_index()\n",
        "    df[\"close\"] = df[\"close\"].astype(float)\n",
        "    df.columns = [\"DATE\", \"TRADING.CODE\", \"LTP\", \"HIGH\", \"LOW\", \"OPENP\", \"CLOSEP\", \"YCP\", \"TRADE\", \"VALUE\", \"VOLUME\"]\n",
        "\n",
        "    # remove leading and trainling blank spaces from column names\n",
        "    df.columns = df.columns.str.strip()\n",
        "\n",
        "    # remove leanding and trailing blank spaces from TRADING.CODEs\n",
        "    df[\"TRADING.CODE\"] = df[\"TRADING.CODE\"].str.strip()\n",
        "\n",
        "    # select the Instrument of your interest\n",
        "    #df = d[d[\"TRADING.CODE\"] == Instrument]\n",
        "\n",
        "    # remove rows with invalid values\n",
        "    df = df[\n",
        "        ~(df[\"LTP\"] == 0)\n",
        "        | (df[\"HIGH\"] == 0)\n",
        "        | (df[\"LOW\"] == 0)\n",
        "        | (df[\"OPENP\"] == 0)\n",
        "        | (df[\"CLOSEP\"] == 0)\n",
        "    ]\n",
        "\n",
        "    # sort the data as per date\n",
        "    df = df.sort_values([\"DATE\"], ascending=True)\n",
        "    \n",
        "    # turn the date column into date format\n",
        "    df['DATE'] = pd.to_datetime(df['DATE'])\n",
        "\n",
        "    # keep only the required columns\n",
        "    clean_data = df[[\"DATE\", \"CLOSEP\"]]\n",
        "\n",
        "    # set the date column as index\n",
        "    clean_data.set_index([\"DATE\"], inplace=True)\n",
        "\n",
        "    # turn the column into a numpy array\n",
        "    close_price = clean_data.values\n",
        "\n",
        "    # set first 85% values as training data and the rest as test data\n",
        "    train = close_price[: int(len(close_price) * 0.85), :]\n",
        "    valid = close_price[int(len(close_price) * 0.85) :, :]\n",
        "\n",
        "    # scaling the values\n",
        "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "    scaled_data = scaler.fit_transform(close_price)\n",
        "\n",
        "    # creating x_train and y_train, seeting the array as 0 dimensional, each time x_train.append is giving 'Back' values with 0 dimension,then set each as\n",
        "    # rows\n",
        "    x_train, y_train = [], []\n",
        "    for i in range(Back, len(train)):\n",
        "        x_train.append(scaled_data[i - Back : i, 0])\n",
        "        y_train.append(scaled_data[i, 0])\n",
        "    x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "\n",
        "    # turn x_train as three dimensional\n",
        "    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
        "\n",
        "    # create and fit the LSTM network\n",
        "    model = Sequential()\n",
        "    model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
        "    model.add(LSTM(units=50))\n",
        "    model.add(Dense(1))\n",
        "\n",
        "    # set name for the saved model\n",
        "    saved_model = Instrument + \"weights.hdf5\"\n",
        "    \n",
        "    # tell the model to minimize mape\n",
        "    model.compile(\n",
        "        loss=\"mean_absolute_percentage_error\", optimizer=\"adam\"\n",
        "    )  \n",
        "    \n",
        "    # tell the model to wait for the next PATIENCE model, if no improvement, stop\n",
        "    ks = tf.keras.callbacks.EarlyStopping(\n",
        "        monitor=\"val_loss\", mode=\"auto\", verbose=1, patience=PATIENCE\n",
        "    )  \n",
        "    \n",
        "    # ask to save the best model\n",
        "    checkpointer = tf.keras.callbacks.ModelCheckpoint(\n",
        "        filepath=saved_model, monitor=\"loss\", verbose=0, save_best_only=True\n",
        "    )  \n",
        "    \n",
        "    # now fit the model\n",
        "    model.fit(\n",
        "        x_train,\n",
        "        y_train,\n",
        "        epochs=TRIAL,\n",
        "        validation_split=0.1,\n",
        "        verbose=0,\n",
        "        callbacks=[ks, checkpointer],\n",
        "    )  \n",
        "    \n",
        "    # now the best model will be loaded from saved file\n",
        "    model = load_model(saved_model)  \n",
        "\n",
        "    # predicting values, using past 'Back' from the train data\n",
        "    inputs = clean_data.values[len(clean_data) - len(valid) - Back :]\n",
        "    inputs = inputs.reshape(-1, 1)\n",
        "    inputs = scaler.transform(inputs)\n",
        "\n",
        "    # creating X_test\n",
        "    X_test = []\n",
        "    for i in range(Back, inputs.shape[0]):\n",
        "        X_test.append(inputs[i - Back : i, 0])\n",
        "    X_test = np.array(X_test)\n",
        "\n",
        "    # turn X_test into 3 dimensional\n",
        "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
        "\n",
        "    # forecasting future values ie calculating y_test\n",
        "    closing_price = model.predict(X_test)\n",
        "    cl = closing_price\n",
        "\n",
        "    # turning the forecasting values back to original scale\n",
        "    closing_price = scaler.inverse_transform(closing_price)\n",
        "\n",
        "    # model evaluate\n",
        "    train_acc = model.evaluate(x_train, y_train, verbose=0)\n",
        "    test_acc = model.evaluate(X_test, cl, verbose=0)\n",
        "    # print('Train: %.3f, Test: %.3f' % (train_acc, test_acc))\n",
        "\n",
        "    # calculating MAPE\n",
        "    rms = np.sqrt(np.mean(np.power((valid - closing_price), 2)))\n",
        "    mape = sum(abs(valid - closing_price) / abs(valid)) * 100 * (1 / len(valid))\n",
        "\n",
        "#### Plot out of sample forecast ####\n",
        "    # plt.figure(figsize=(20, 10))\n",
        "    train = clean_data[: int(len(close_price) * 0.85)]\n",
        "    valid = clean_data[int(len(close_price) * 0.85) :]\n",
        "    valid[\"Predictions\"] = closing_price\n",
        "    # plt.plot(train[\"CLOSEP\"])\n",
        "    # plt.plot(valid[[\"CLOSEP\", \"Predictions\"]])\n",
        "    # plt.title(\"Out of Sample Forecast\")\n",
        "    # plt.xlabel(\"Date\")\n",
        "    # plt.ylabel(\"Price\")\n",
        "    # plt.legend([\"Train Set\", \"Test Set\", \"Forecasted Price\"], prop={\"size\":15})\n",
        "\n",
        "#### If you need the Plotly Interactive Chart ####\n",
        "    trace1 = go.Scatter(x=train.index, y=train[\"CLOSEP\"], mode=\"lines\", name=\"Train Set\")\n",
        "    trace2 = go.Scatter(x=valid.index, y=valid[\"CLOSEP\"], mode=\"lines\", name=\"Test Set\")\n",
        "    trace3 = go.Scatter(x=valid.index, y=valid[\"Predictions\"], mode=\"lines\", name=\"Predicted Values\")\n",
        "    data = [trace1, trace2, trace3]\n",
        "    layout = go.Layout(\n",
        "        title = \"Out of Sample Forecast\", \n",
        "        xaxis = dict(title = \"Date\"), \n",
        "        yaxis = dict(title = \"Price\"),\n",
        "        legend = dict(yanchor = \"top\", xanchor = \"left\") \n",
        "    )\n",
        "    \n",
        "    fig_test = go.Figure(data = data, layout = layout)\n",
        "    fig_test.show()\n",
        "    \n",
        "    # out of sample (beyond test set) forecast, next 30 business days\n",
        "    last_values = scaled_data[-Back:]  # take last 'Back' values\n",
        "    last_values = last_values.reshape((1, Back, 1))  # reshape\n",
        "    yhat = np.zeros(shape=(30, 1))\n",
        "\n",
        "    # run for loop for creating one forecast at a time and then use it to forecast the next\n",
        "    for i in range(0, 30):\n",
        "        yhat[i] = model.predict(last_values, verbose=0)\n",
        "        last_values = np.append(last_values, yhat[i])[-Back:].reshape(1, Back, 1)\n",
        "\n",
        "    yx = scaler.inverse_transform(yhat)  # inverse the variable transformation\n",
        "    yx = np.concatenate(yx).ravel().tolist()  # unlist the forecast data\n",
        "    yx = [round(w, 1) for w in yx]  # rounding the data to .10\n",
        "\n",
        "    # create the date index of the forecasted values\n",
        "    weekmask = \"Sun Mon Tue Wed Thu\"\n",
        "    custombday = pd.offsets.CustomBusinessDay(weekmask=weekmask)\n",
        "    pday = pd.bdate_range(\n",
        "        start=df.iloc[clean_data.shape[0] - 1, 0] + datetime.timedelta(days=1),\n",
        "        end=df.iloc[clean_data.shape[0] - 1, 0] + datetime.timedelta(days=365),\n",
        "        freq=custombday,\n",
        "    ).tolist()[0:30]\n",
        "\n",
        "    # turn the forecasted values into a dataframe\n",
        "    forecast_data = pd.DataFrame({\"Date\": pday, \"Forecast\": yx})\n",
        "    forecast_data.set_index(\"Date\", inplace=True)\n",
        "\n",
        "    # now plot the forecast along with the original data\n",
        "    comb_data = pd.merge(\n",
        "        valid, forecast_data, left_index=True, right_index=True, how=\"outer\"\n",
        "    )\n",
        "    \n",
        "    # plt.figure(figsize=(20, 10))\n",
        "    # plt.plot(comb_data[[\"CLOSEP\", \"Forecast\"]])\n",
        "    # plt.title(\"Future Value Forecast\")\n",
        "    # plt.xlabel(\"Date\")\n",
        "    # plt.ylabel(\"Price\")\n",
        "    # plt.legend([\"Actual Price\", \"Forecasted Price\"], prop={\"size\":15})\n",
        "\n",
        "    #### For Plotly Interactive Charts ####\n",
        "    trace1 = go.Scatter(x=comb_data.index, y=comb_data[\"CLOSEP\"], mode = \"lines\", name = \"Actual Price\")\n",
        "    trace2 = go.Scatter(x = comb_data.index, y = comb_data[\"Forecast\"], mode = \"lines\", name = \"Forecasted Values\")\n",
        "    data = [trace1, trace2]\n",
        "    layout = go.Layout(\n",
        "        title = \"Future Value Forecast\", \n",
        "        xaxis = dict(title = \"Date\"), \n",
        "        yaxis = dict(title = \"Price\")\n",
        "        #width = 900\n",
        "    )\n",
        "    fig_forecast = go.Figure(data=data, layout=layout)\n",
        "    fig_forecast.show()\n",
        "\n",
        "    # Print RMS & MAPE of the Model \n",
        "    print(\"The Root Mean Square of the Model is: %.3f\" % (rms))\n",
        "    print(\"The Mean Absolute Percentage Error of the Model is: %.3f\" % (mape))\n",
        "\n",
        "    # the forecasted data as a output\n",
        "    return forecast_data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nEomaD5Q-GI"
      },
      "source": [
        "# Now set the Parameters and Run"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JC74lC6NQ-GS",
        "outputId": "1bd86f6d-99b7-480e-eccb-b8811d3460eb"
      },
      "source": [
        "# Instrument = input Instrument Name\n",
        "# TRIAL = number of Epoch (trials),\n",
        "# PATIENCE = after how many trials without any improvement in result the system should stop trial\n",
        "# Back = number of previous data points to be used for forecasting\n",
        "Stock_Forecast(Instrument=\"ACI\", TRIAL=10000, PATIENCE=500, Back=200)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 00868: early stopping\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:189: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"f76f1ef7-79ee-46ce-b63b-a24e78daca4e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"f76f1ef7-79ee-46ce-b63b-a24e78daca4e\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        'f76f1ef7-79ee-46ce-b63b-a24e78daca4e',\n",
              "                        [{\"mode\": \"lines\", \"name\": \"Train Set\", \"type\": \"scatter\", \"x\": [\"2019-06-09T00:00:00\", \"2019-06-10T00:00:00\", \"2019-06-11T00:00:00\", \"2019-06-12T00:00:00\", \"2019-06-13T00:00:00\", \"2019-06-16T00:00:00\", \"2019-06-17T00:00:00\", \"2019-06-18T00:00:00\", \"2019-06-19T00:00:00\", \"2019-06-20T00:00:00\", \"2019-06-23T00:00:00\", \"2019-06-24T00:00:00\", \"2019-06-25T00:00:00\", \"2019-06-26T00:00:00\", \"2019-06-27T00:00:00\", \"2019-06-30T00:00:00\", \"2019-07-02T00:00:00\", \"2019-07-03T00:00:00\", \"2019-07-04T00:00:00\", \"2019-07-07T00:00:00\", \"2019-07-08T00:00:00\", \"2019-07-09T00:00:00\", \"2019-07-10T00:00:00\", \"2019-07-11T00:00:00\", \"2019-07-14T00:00:00\", \"2019-07-15T00:00:00\", \"2019-07-16T00:00:00\", \"2019-07-17T00:00:00\", \"2019-07-18T00:00:00\", \"2019-07-21T00:00:00\", \"2019-07-22T00:00:00\", \"2019-07-23T00:00:00\", \"2019-07-24T00:00:00\", \"2019-07-25T00:00:00\", \"2019-07-28T00:00:00\", \"2019-07-29T00:00:00\", \"2019-07-30T00:00:00\", \"2019-07-31T00:00:00\", \"2019-08-01T00:00:00\", \"2019-08-04T00:00:00\", \"2019-08-05T00:00:00\", \"2019-08-06T00:00:00\", \"2019-08-07T00:00:00\", \"2019-08-08T00:00:00\", \"2019-08-18T00:00:00\", \"2019-08-19T00:00:00\", \"2019-08-20T00:00:00\", \"2019-08-21T00:00:00\", \"2019-08-22T00:00:00\", \"2019-08-25T00:00:00\", \"2019-08-26T00:00:00\", \"2019-08-27T00:00:00\", \"2019-08-28T00:00:00\", \"2019-08-29T00:00:00\", \"2019-09-01T00:00:00\", \"2019-09-02T00:00:00\", \"2019-09-03T00:00:00\", \"2019-09-04T00:00:00\", \"2019-09-05T00:00:00\", \"2019-09-08T00:00:00\", \"2019-09-09T00:00:00\", \"2019-09-11T00:00:00\", \"2019-09-12T00:00:00\", \"2019-09-15T00:00:00\", \"2019-09-16T00:00:00\", \"2019-09-17T00:00:00\", \"2019-09-18T00:00:00\", \"2019-09-19T00:00:00\", \"2019-09-22T00:00:00\", \"2019-09-23T00:00:00\", \"2019-09-24T00:00:00\", \"2019-09-25T00:00:00\", \"2019-09-26T00:00:00\", \"2019-09-29T00:00:00\", \"2019-09-30T00:00:00\", \"2019-10-01T00:00:00\", \"2019-10-02T00:00:00\", \"2019-10-03T00:00:00\", \"2019-10-06T00:00:00\", \"2019-10-07T00:00:00\", \"2019-10-09T00:00:00\", \"2019-10-10T00:00:00\", \"2019-10-13T00:00:00\", \"2019-10-14T00:00:00\", \"2019-10-15T00:00:00\", \"2019-10-16T00:00:00\", \"2019-10-17T00:00:00\", \"2019-10-20T00:00:00\", \"2019-10-21T00:00:00\", \"2019-10-22T00:00:00\", \"2019-10-23T00:00:00\", \"2019-10-24T00:00:00\", \"2019-10-27T00:00:00\", \"2019-10-28T00:00:00\", \"2019-10-29T00:00:00\", \"2019-10-30T00:00:00\", \"2019-10-31T00:00:00\", \"2019-11-03T00:00:00\", \"2019-11-04T00:00:00\", \"2019-11-05T00:00:00\", \"2019-11-06T00:00:00\", \"2019-11-07T00:00:00\", \"2019-11-11T00:00:00\", \"2019-11-12T00:00:00\", \"2019-11-13T00:00:00\", \"2019-11-14T00:00:00\", \"2019-11-17T00:00:00\", \"2019-11-18T00:00:00\", \"2019-11-19T00:00:00\", \"2019-11-20T00:00:00\", \"2019-11-21T00:00:00\", \"2019-11-24T00:00:00\", \"2019-11-25T00:00:00\", \"2019-11-26T00:00:00\", \"2019-11-27T00:00:00\", \"2019-11-28T00:00:00\", \"2019-12-01T00:00:00\", \"2019-12-02T00:00:00\", \"2019-12-03T00:00:00\", \"2019-12-04T00:00:00\", \"2019-12-05T00:00:00\", \"2019-12-08T00:00:00\", \"2019-12-09T00:00:00\", \"2019-12-10T00:00:00\", \"2019-12-11T00:00:00\", \"2019-12-12T00:00:00\", \"2019-12-15T00:00:00\", \"2019-12-17T00:00:00\", \"2019-12-18T00:00:00\", \"2019-12-19T00:00:00\", \"2019-12-22T00:00:00\", \"2019-12-23T00:00:00\", \"2019-12-24T00:00:00\", \"2019-12-26T00:00:00\", \"2019-12-29T00:00:00\", \"2019-12-30T00:00:00\", \"2020-01-01T00:00:00\", \"2020-01-02T00:00:00\", \"2020-01-05T00:00:00\", \"2020-01-06T00:00:00\", \"2020-01-07T00:00:00\", \"2020-01-08T00:00:00\", \"2020-01-09T00:00:00\", \"2020-01-12T00:00:00\", \"2020-01-13T00:00:00\", \"2020-01-14T00:00:00\", \"2020-01-15T00:00:00\", \"2020-01-16T00:00:00\", \"2020-01-19T00:00:00\", \"2020-01-20T00:00:00\", \"2020-01-21T00:00:00\", \"2020-01-22T00:00:00\", \"2020-01-23T00:00:00\", \"2020-01-26T00:00:00\", \"2020-01-27T00:00:00\", \"2020-01-28T00:00:00\", \"2020-01-29T00:00:00\", \"2020-01-30T00:00:00\", \"2020-02-02T00:00:00\", \"2020-02-03T00:00:00\", \"2020-02-04T00:00:00\", \"2020-02-05T00:00:00\", \"2020-02-06T00:00:00\", \"2020-02-09T00:00:00\", \"2020-02-10T00:00:00\", \"2020-02-11T00:00:00\", \"2020-02-12T00:00:00\", \"2020-02-13T00:00:00\", \"2020-02-16T00:00:00\", \"2020-02-17T00:00:00\", \"2020-02-18T00:00:00\", \"2020-02-19T00:00:00\", \"2020-02-20T00:00:00\", \"2020-02-23T00:00:00\", \"2020-02-24T00:00:00\", \"2020-02-25T00:00:00\", \"2020-02-26T00:00:00\", \"2020-02-27T00:00:00\", \"2020-03-01T00:00:00\", \"2020-03-02T00:00:00\", \"2020-03-03T00:00:00\", \"2020-03-04T00:00:00\", \"2020-03-05T00:00:00\", \"2020-03-08T00:00:00\", \"2020-03-09T00:00:00\", \"2020-03-10T00:00:00\", \"2020-03-11T00:00:00\", \"2020-03-12T00:00:00\", \"2020-03-15T00:00:00\", \"2020-03-16T00:00:00\", \"2020-03-18T00:00:00\", \"2020-03-19T00:00:00\", \"2020-03-22T00:00:00\", \"2020-03-23T00:00:00\", \"2020-03-24T00:00:00\", \"2020-03-25T00:00:00\", \"2020-05-31T00:00:00\", \"2020-06-01T00:00:00\", \"2020-06-02T00:00:00\", \"2020-06-03T00:00:00\", \"2020-06-04T00:00:00\", \"2020-06-07T00:00:00\", \"2020-06-08T00:00:00\", \"2020-06-09T00:00:00\", \"2020-06-10T00:00:00\", \"2020-06-11T00:00:00\", \"2020-06-14T00:00:00\", \"2020-06-15T00:00:00\", \"2020-06-16T00:00:00\", \"2020-06-17T00:00:00\", \"2020-06-18T00:00:00\", \"2020-06-21T00:00:00\", \"2020-06-22T00:00:00\", \"2020-06-23T00:00:00\", \"2020-06-24T00:00:00\", \"2020-06-25T00:00:00\", \"2020-06-28T00:00:00\", \"2020-06-29T00:00:00\", \"2020-06-30T00:00:00\", \"2020-07-02T00:00:00\", \"2020-07-05T00:00:00\", \"2020-07-06T00:00:00\", \"2020-07-07T00:00:00\", \"2020-07-08T00:00:00\", \"2020-07-09T00:00:00\", \"2020-07-12T00:00:00\", \"2020-07-13T00:00:00\", \"2020-07-14T00:00:00\", \"2020-07-15T00:00:00\", \"2020-07-16T00:00:00\", \"2020-07-19T00:00:00\", \"2020-07-20T00:00:00\", \"2020-07-21T00:00:00\", \"2020-07-22T00:00:00\", \"2020-07-23T00:00:00\", \"2020-07-26T00:00:00\", \"2020-07-27T00:00:00\", \"2020-07-28T00:00:00\", \"2020-07-29T00:00:00\", \"2020-07-30T00:00:00\", \"2020-08-03T00:00:00\", \"2020-08-04T00:00:00\", \"2020-08-05T00:00:00\", \"2020-08-06T00:00:00\", \"2020-08-09T00:00:00\", \"2020-08-10T00:00:00\", \"2020-08-12T00:00:00\", \"2020-08-13T00:00:00\", \"2020-08-16T00:00:00\", \"2020-08-17T00:00:00\", \"2020-08-18T00:00:00\", \"2020-08-19T00:00:00\", \"2020-08-20T00:00:00\", \"2020-08-23T00:00:00\", \"2020-08-24T00:00:00\", \"2020-08-25T00:00:00\", \"2020-08-26T00:00:00\", \"2020-08-27T00:00:00\", \"2020-08-31T00:00:00\", \"2020-09-01T00:00:00\", \"2020-09-02T00:00:00\", \"2020-09-03T00:00:00\", \"2020-09-06T00:00:00\", \"2020-09-07T00:00:00\", \"2020-09-08T00:00:00\", \"2020-09-09T00:00:00\", \"2020-09-10T00:00:00\", \"2020-09-13T00:00:00\", \"2020-09-14T00:00:00\", \"2020-09-15T00:00:00\", \"2020-09-16T00:00:00\", \"2020-09-17T00:00:00\", \"2020-09-20T00:00:00\", \"2020-09-21T00:00:00\", \"2020-09-22T00:00:00\", \"2020-09-23T00:00:00\", \"2020-09-24T00:00:00\", \"2020-09-27T00:00:00\", \"2020-09-28T00:00:00\", \"2020-09-29T00:00:00\", \"2020-09-30T00:00:00\", \"2020-10-01T00:00:00\", \"2020-10-04T00:00:00\", \"2020-10-05T00:00:00\", \"2020-10-06T00:00:00\", \"2020-10-07T00:00:00\", \"2020-10-08T00:00:00\", \"2020-10-11T00:00:00\", \"2020-10-12T00:00:00\", \"2020-10-13T00:00:00\", \"2020-10-14T00:00:00\", \"2020-10-15T00:00:00\", \"2020-10-18T00:00:00\", \"2020-10-19T00:00:00\", \"2020-10-20T00:00:00\", \"2020-10-21T00:00:00\", \"2020-10-22T00:00:00\", \"2020-10-25T00:00:00\", \"2020-10-27T00:00:00\", \"2020-10-28T00:00:00\", \"2020-10-29T00:00:00\", \"2020-11-01T00:00:00\", \"2020-11-02T00:00:00\", \"2020-11-03T00:00:00\", \"2020-11-04T00:00:00\", \"2020-11-05T00:00:00\", \"2020-11-08T00:00:00\", \"2020-11-09T00:00:00\", \"2020-11-10T00:00:00\", \"2020-11-11T00:00:00\", \"2020-11-12T00:00:00\", \"2020-11-15T00:00:00\", \"2020-11-16T00:00:00\", \"2020-11-17T00:00:00\", \"2020-11-18T00:00:00\", \"2020-11-19T00:00:00\", \"2020-11-22T00:00:00\", \"2020-11-23T00:00:00\", \"2020-11-24T00:00:00\", \"2020-11-25T00:00:00\", \"2020-11-26T00:00:00\", \"2020-11-29T00:00:00\", \"2020-11-30T00:00:00\", \"2020-12-01T00:00:00\", \"2020-12-02T00:00:00\", \"2020-12-03T00:00:00\", \"2020-12-06T00:00:00\", \"2020-12-07T00:00:00\", \"2020-12-08T00:00:00\", \"2020-12-09T00:00:00\", \"2020-12-10T00:00:00\", \"2020-12-13T00:00:00\", \"2020-12-14T00:00:00\", \"2020-12-15T00:00:00\", \"2020-12-17T00:00:00\", \"2020-12-20T00:00:00\", \"2020-12-21T00:00:00\", \"2020-12-22T00:00:00\", \"2020-12-23T00:00:00\", \"2020-12-24T00:00:00\", \"2020-12-27T00:00:00\", \"2020-12-28T00:00:00\", \"2020-12-29T00:00:00\", \"2020-12-30T00:00:00\", \"2021-01-03T00:00:00\", \"2021-01-04T00:00:00\", \"2021-01-05T00:00:00\", \"2021-01-06T00:00:00\", \"2021-01-07T00:00:00\", \"2021-01-10T00:00:00\", \"2021-01-11T00:00:00\", \"2021-01-12T00:00:00\", \"2021-01-13T00:00:00\", \"2021-01-14T00:00:00\", \"2021-01-17T00:00:00\", \"2021-01-18T00:00:00\", \"2021-01-19T00:00:00\", \"2021-01-20T00:00:00\", \"2021-01-21T00:00:00\", \"2021-01-24T00:00:00\", \"2021-01-25T00:00:00\", \"2021-01-26T00:00:00\", \"2021-01-27T00:00:00\", \"2021-01-28T00:00:00\", \"2021-01-31T00:00:00\", \"2021-02-01T00:00:00\", \"2021-02-02T00:00:00\", \"2021-02-03T00:00:00\", \"2021-02-04T00:00:00\", \"2021-02-07T00:00:00\", \"2021-02-08T00:00:00\", \"2021-02-09T00:00:00\", \"2021-02-10T00:00:00\", \"2021-02-11T00:00:00\", \"2021-02-14T00:00:00\", \"2021-02-15T00:00:00\"], \"y\": [253.0, 254.9, 263.7, 261.6, 262.6, 265.5, 275.9, 274.7, 272.4, 272.2, 269.9, 265.6, 267.7, 269.8, 268.9, 274.7, 273.0, 273.9, 271.8, 269.6, 266.8, 256.9, 255.1, 254.1, 254.6, 248.4, 250.0, 246.1, 246.2, 243.4, 243.2, 251.6, 250.7, 250.0, 254.1, 254.6, 254.0, 255.4, 258.0, 251.6, 253.5, 259.6, 272.2, 278.1, 302.4, 318.4, 303.7, 291.8, 295.6, 292.4, 283.0, 296.5, 288.9, 286.1, 281.3, 274.0, 274.9, 277.3, 281.8, 280.4, 276.7, 272.7, 269.6, 269.1, 270.6, 269.6, 262.3, 262.9, 264.1, 264.8, 261.8, 259.9, 257.6, 259.1, 255.2, 260.3, 255.5, 257.6, 256.5, 253.8, 253.7, 250.1, 240.4, 230.1, 236.1, 229.9, 228.9, 229.7, 227.0, 219.3, 217.8, 229.7, 231.9, 226.4, 219.4, 217.7, 217.7, 224.1, 224.6, 223.9, 227.8, 237.9, 229.2, 242.5, 232.5, 232.9, 233.9, 230.2, 230.3, 231.3, 235.7, 244.7, 262.3, 268.1, 265.9, 271.9, 274.6, 272.5, 272.5, 224.0, 218.7, 207.2, 199.9, 192.9, 192.4, 190.8, 190.7, 190.4, 186.7, 186.9, 185.3, 184.0, 183.1, 184.1, 181.2, 181.5, 182.6, 195.7, 191.1, 186.0, 186.2, 184.2, 182.9, 185.4, 181.6, 178.2, 178.8, 182.9, 189.9, 193.1, 190.3, 189.1, 199.8, 201.1, 197.9, 196.8, 198.0, 182.9, 180.9, 175.8, 177.5, 180.9, 179.9, 179.3, 178.3, 180.6, 179.2, 181.1, 198.6, 202.0, 201.6, 196.1, 194.3, 194.5, 188.9, 190.6, 189.1, 186.8, 182.4, 183.0, 180.8, 181.1, 181.9, 179.9, 172.7, 179.8, 189.4, 189.9, 181.2, 175.4, 168.4, 181.7, 190.3, 202.2, 193.0, 196.6, 216.2, 209.6, 202.5, 196.3, 192.0, 194.4, 190.4, 192.1, 189.1, 194.1, 190.5, 185.8, 186.0, 185.9, 192.5, 191.5, 191.5, 192.4, 192.5, 193.1, 197.6, 200.8, 204.2, 198.0, 192.6, 197.4, 201.9, 200.7, 208.0, 213.7, 209.3, 207.7, 208.1, 208.0, 205.4, 206.4, 209.4, 210.3, 210.7, 223.5, 234.5, 229.5, 238.8, 242.3, 249.0, 255.9, 250.6, 261.9, 269.0, 265.2, 267.5, 269.4, 284.0, 281.9, 276.3, 279.0, 276.5, 274.9, 270.4, 273.1, 274.7, 273.4, 270.5, 270.4, 266.3, 259.6, 252.9, 255.4, 256.8, 257.1, 257.1, 266.3, 264.5, 267.8, 265.2, 264.9, 263.3, 257.4, 256.2, 256.3, 260.0, 255.8, 254.9, 254.7, 250.6, 259.9, 255.4, 245.8, 249.0, 245.8, 251.3, 247.1, 243.3, 245.2, 246.0, 245.5, 256.5, 256.9, 253.8, 257.6, 251.2, 253.5, 252.8, 251.7, 248.3, 249.1, 256.8, 256.0, 250.7, 248.5, 256.4, 254.3, 262.8, 256.5, 253.3, 251.3, 250.6, 251.9, 252.3, 261.8, 258.5, 259.5, 261.5, 265.9, 266.0, 264.7, 266.6, 266.6, 244.3, 242.2, 238.8, 245.5, 247.2, 245.1, 245.2, 245.0, 245.3, 249.7, 248.7, 240.4, 240.5, 240.2, 240.8, 241.0, 239.1, 244.1, 247.2, 246.0, 248.7, 248.4, 247.4, 246.7, 246.4, 248.5, 259.3, 255.3, 255.5, 256.6, 257.4, 261.9, 266.0, 262.3, 263.9, 265.9, 260.4, 256.6, 257.5, 257.1, 259.8, 256.2, 252.5, 252.8, 252.7, 251.3, 247.2, 247.6, 244.3, 241.4, 241.9, 239.6]}, {\"mode\": \"lines\", \"name\": \"Test Set\", \"type\": \"scatter\", \"x\": [\"2021-02-16T00:00:00\", \"2021-02-17T00:00:00\", \"2021-02-18T00:00:00\", \"2021-02-22T00:00:00\", \"2021-02-23T00:00:00\", \"2021-02-24T00:00:00\", \"2021-02-25T00:00:00\", \"2021-02-28T00:00:00\", \"2021-03-01T00:00:00\", \"2021-03-02T00:00:00\", \"2021-03-03T00:00:00\", \"2021-03-04T00:00:00\", \"2021-03-07T00:00:00\", \"2021-03-08T00:00:00\", \"2021-03-09T00:00:00\", \"2021-03-10T00:00:00\", \"2021-03-11T00:00:00\", \"2021-03-14T00:00:00\", \"2021-03-15T00:00:00\", \"2021-03-16T00:00:00\", \"2021-03-18T00:00:00\", \"2021-03-21T00:00:00\", \"2021-03-22T00:00:00\", \"2021-03-23T00:00:00\", \"2021-03-24T00:00:00\", \"2021-03-25T00:00:00\", \"2021-03-28T00:00:00\", \"2021-03-29T00:00:00\", \"2021-03-31T00:00:00\", \"2021-04-01T00:00:00\", \"2021-04-04T00:00:00\", \"2021-04-05T00:00:00\", \"2021-04-06T00:00:00\", \"2021-04-07T00:00:00\", \"2021-04-08T00:00:00\", \"2021-04-11T00:00:00\", \"2021-04-12T00:00:00\", \"2021-04-13T00:00:00\", \"2021-04-15T00:00:00\", \"2021-04-18T00:00:00\", \"2021-04-19T00:00:00\", \"2021-04-20T00:00:00\", \"2021-04-21T00:00:00\", \"2021-04-22T00:00:00\", \"2021-04-25T00:00:00\", \"2021-04-26T00:00:00\", \"2021-04-27T00:00:00\", \"2021-04-28T00:00:00\", \"2021-04-29T00:00:00\", \"2021-05-02T00:00:00\", \"2021-05-03T00:00:00\", \"2021-05-04T00:00:00\", \"2021-05-05T00:00:00\", \"2021-05-06T00:00:00\", \"2021-05-09T00:00:00\", \"2021-05-11T00:00:00\", \"2021-05-12T00:00:00\", \"2021-05-16T00:00:00\", \"2021-05-17T00:00:00\", \"2021-05-18T00:00:00\", \"2021-05-19T00:00:00\", \"2021-05-20T00:00:00\", \"2021-05-23T00:00:00\", \"2021-05-24T00:00:00\", \"2021-05-25T00:00:00\", \"2021-05-27T00:00:00\", \"2021-05-30T00:00:00\"], \"y\": [242.6, 241.0, 242.0, 240.8, 241.1, 243.1, 243.0, 242.4, 242.6, 243.7, 243.2, 246.8, 253.3, 255.4, 253.5, 247.9, 249.7, 249.3, 249.5, 253.7, 250.3, 247.6, 244.6, 244.7, 242.6, 243.5, 242.9, 243.2, 240.2, 242.1, 227.8, 230.3, 236.0, 237.2, 232.0, 227.3, 230.1, 236.0, 236.8, 238.8, 242.2, 243.4, 243.1, 242.5, 243.6, 242.7, 243.2, 242.5, 241.9, 243.7, 243.8, 242.6, 241.5, 247.2, 255.2, 256.0, 254.3, 254.5, 255.2, 265.4, 262.1, 254.6, 255.2, 256.2, 249.3, 247.8, 246.4]}, {\"mode\": \"lines\", \"name\": \"Predicted Values\", \"type\": \"scatter\", \"x\": [\"2021-02-16T00:00:00\", \"2021-02-17T00:00:00\", \"2021-02-18T00:00:00\", \"2021-02-22T00:00:00\", \"2021-02-23T00:00:00\", \"2021-02-24T00:00:00\", \"2021-02-25T00:00:00\", \"2021-02-28T00:00:00\", \"2021-03-01T00:00:00\", \"2021-03-02T00:00:00\", \"2021-03-03T00:00:00\", \"2021-03-04T00:00:00\", \"2021-03-07T00:00:00\", \"2021-03-08T00:00:00\", \"2021-03-09T00:00:00\", \"2021-03-10T00:00:00\", \"2021-03-11T00:00:00\", \"2021-03-14T00:00:00\", \"2021-03-15T00:00:00\", \"2021-03-16T00:00:00\", \"2021-03-18T00:00:00\", \"2021-03-21T00:00:00\", \"2021-03-22T00:00:00\", \"2021-03-23T00:00:00\", \"2021-03-24T00:00:00\", \"2021-03-25T00:00:00\", \"2021-03-28T00:00:00\", \"2021-03-29T00:00:00\", \"2021-03-31T00:00:00\", \"2021-04-01T00:00:00\", \"2021-04-04T00:00:00\", \"2021-04-05T00:00:00\", \"2021-04-06T00:00:00\", \"2021-04-07T00:00:00\", \"2021-04-08T00:00:00\", \"2021-04-11T00:00:00\", \"2021-04-12T00:00:00\", \"2021-04-13T00:00:00\", \"2021-04-15T00:00:00\", \"2021-04-18T00:00:00\", \"2021-04-19T00:00:00\", \"2021-04-20T00:00:00\", \"2021-04-21T00:00:00\", \"2021-04-22T00:00:00\", \"2021-04-25T00:00:00\", \"2021-04-26T00:00:00\", \"2021-04-27T00:00:00\", \"2021-04-28T00:00:00\", \"2021-04-29T00:00:00\", \"2021-05-02T00:00:00\", \"2021-05-03T00:00:00\", \"2021-05-04T00:00:00\", \"2021-05-05T00:00:00\", \"2021-05-06T00:00:00\", \"2021-05-09T00:00:00\", \"2021-05-11T00:00:00\", \"2021-05-12T00:00:00\", \"2021-05-16T00:00:00\", \"2021-05-17T00:00:00\", \"2021-05-18T00:00:00\", \"2021-05-19T00:00:00\", \"2021-05-20T00:00:00\", \"2021-05-23T00:00:00\", \"2021-05-24T00:00:00\", \"2021-05-25T00:00:00\", \"2021-05-27T00:00:00\", \"2021-05-30T00:00:00\"], \"y\": [242.0552520751953, 242.80526733398438, 241.12364196777344, 241.79978942871094, 240.8234405517578, 241.31866455078125, 242.08099365234375, 242.32003784179688, 242.65260314941406, 243.23155212402344, 244.16690063476562, 244.42147827148438, 246.739990234375, 249.9564666748047, 251.78311157226562, 252.5877227783203, 251.40245056152344, 253.28829956054688, 252.4635009765625, 253.21636962890625, 254.8678741455078, 253.15869140625, 252.98947143554688, 251.22592163085938, 251.27359008789062, 249.38563537597656, 249.62228393554688, 248.27688598632812, 248.15304565429688, 246.01812744140625, 246.6217803955078, 239.2621307373047, 240.564208984375, 240.0498809814453, 239.79641723632812, 237.15707397460938, 235.1519775390625, 235.43313598632812, 236.82415771484375, 236.88856506347656, 238.36253356933594, 240.35848999023438, 241.94549560546875, 243.3440399169922, 244.5909423828125, 246.41258239746094, 247.03738403320312, 248.48828125, 248.7904052734375, 249.39700317382812, 250.62588500976562, 250.92941284179688, 251.02525329589844, 250.86717224121094, 253.53378295898438, 256.5364074707031, 257.1689453125, 257.607666015625, 258.0904235839844, 258.377197265625, 263.30499267578125, 260.80572509765625, 260.14166259765625, 260.80926513671875, 260.9185485839844, 257.40069580078125, 256.7554931640625]}],\n",
              "                        {\"legend\": {\"xanchor\": \"left\", \"yanchor\": \"top\"}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Out of Sample Forecast\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"Price\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('f76f1ef7-79ee-46ce-b63b-a24e78daca4e');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>\n",
              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"d8c0c5ba-e7e2-4439-9cdd-564f35435fb2\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"d8c0c5ba-e7e2-4439-9cdd-564f35435fb2\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        'd8c0c5ba-e7e2-4439-9cdd-564f35435fb2',\n",
              "                        [{\"mode\": \"lines\", \"name\": \"Actual Price\", \"type\": \"scatter\", \"x\": [\"2021-02-16T00:00:00\", \"2021-02-17T00:00:00\", \"2021-02-18T00:00:00\", \"2021-02-22T00:00:00\", \"2021-02-23T00:00:00\", \"2021-02-24T00:00:00\", \"2021-02-25T00:00:00\", \"2021-02-28T00:00:00\", \"2021-03-01T00:00:00\", \"2021-03-02T00:00:00\", \"2021-03-03T00:00:00\", \"2021-03-04T00:00:00\", \"2021-03-07T00:00:00\", \"2021-03-08T00:00:00\", \"2021-03-09T00:00:00\", \"2021-03-10T00:00:00\", \"2021-03-11T00:00:00\", \"2021-03-14T00:00:00\", \"2021-03-15T00:00:00\", \"2021-03-16T00:00:00\", \"2021-03-18T00:00:00\", \"2021-03-21T00:00:00\", \"2021-03-22T00:00:00\", \"2021-03-23T00:00:00\", \"2021-03-24T00:00:00\", \"2021-03-25T00:00:00\", \"2021-03-28T00:00:00\", \"2021-03-29T00:00:00\", \"2021-03-31T00:00:00\", \"2021-04-01T00:00:00\", \"2021-04-04T00:00:00\", \"2021-04-05T00:00:00\", \"2021-04-06T00:00:00\", \"2021-04-07T00:00:00\", \"2021-04-08T00:00:00\", \"2021-04-11T00:00:00\", \"2021-04-12T00:00:00\", \"2021-04-13T00:00:00\", \"2021-04-15T00:00:00\", \"2021-04-18T00:00:00\", \"2021-04-19T00:00:00\", \"2021-04-20T00:00:00\", \"2021-04-21T00:00:00\", \"2021-04-22T00:00:00\", \"2021-04-25T00:00:00\", \"2021-04-26T00:00:00\", \"2021-04-27T00:00:00\", \"2021-04-28T00:00:00\", \"2021-04-29T00:00:00\", \"2021-05-02T00:00:00\", \"2021-05-03T00:00:00\", \"2021-05-04T00:00:00\", \"2021-05-05T00:00:00\", \"2021-05-06T00:00:00\", \"2021-05-09T00:00:00\", \"2021-05-11T00:00:00\", \"2021-05-12T00:00:00\", \"2021-05-16T00:00:00\", \"2021-05-17T00:00:00\", \"2021-05-18T00:00:00\", \"2021-05-19T00:00:00\", \"2021-05-20T00:00:00\", \"2021-05-23T00:00:00\", \"2021-05-24T00:00:00\", \"2021-05-25T00:00:00\", \"2021-05-27T00:00:00\", \"2021-05-30T00:00:00\", \"2021-05-31T00:00:00\", \"2021-06-01T00:00:00\", \"2021-06-02T00:00:00\", \"2021-06-03T00:00:00\", \"2021-06-06T00:00:00\", \"2021-06-07T00:00:00\", \"2021-06-08T00:00:00\", \"2021-06-09T00:00:00\", \"2021-06-10T00:00:00\", \"2021-06-13T00:00:00\", \"2021-06-14T00:00:00\", \"2021-06-15T00:00:00\", \"2021-06-16T00:00:00\", \"2021-06-17T00:00:00\", \"2021-06-20T00:00:00\", \"2021-06-21T00:00:00\", \"2021-06-22T00:00:00\", \"2021-06-23T00:00:00\", \"2021-06-24T00:00:00\", \"2021-06-27T00:00:00\", \"2021-06-28T00:00:00\", \"2021-06-29T00:00:00\", \"2021-06-30T00:00:00\", \"2021-07-01T00:00:00\", \"2021-07-04T00:00:00\", \"2021-07-05T00:00:00\", \"2021-07-06T00:00:00\", \"2021-07-07T00:00:00\", \"2021-07-08T00:00:00\", \"2021-07-11T00:00:00\"], \"y\": [242.6, 241.0, 242.0, 240.8, 241.1, 243.1, 243.0, 242.4, 242.6, 243.7, 243.2, 246.8, 253.3, 255.4, 253.5, 247.9, 249.7, 249.3, 249.5, 253.7, 250.3, 247.6, 244.6, 244.7, 242.6, 243.5, 242.9, 243.2, 240.2, 242.1, 227.8, 230.3, 236.0, 237.2, 232.0, 227.3, 230.1, 236.0, 236.8, 238.8, 242.2, 243.4, 243.1, 242.5, 243.6, 242.7, 243.2, 242.5, 241.9, 243.7, 243.8, 242.6, 241.5, 247.2, 255.2, 256.0, 254.3, 254.5, 255.2, 265.4, 262.1, 254.6, 255.2, 256.2, 249.3, 247.8, 246.4, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null]}, {\"mode\": \"lines\", \"name\": \"Forecasted Values\", \"type\": \"scatter\", \"x\": [\"2021-02-16T00:00:00\", \"2021-02-17T00:00:00\", \"2021-02-18T00:00:00\", \"2021-02-22T00:00:00\", \"2021-02-23T00:00:00\", \"2021-02-24T00:00:00\", \"2021-02-25T00:00:00\", \"2021-02-28T00:00:00\", \"2021-03-01T00:00:00\", \"2021-03-02T00:00:00\", \"2021-03-03T00:00:00\", \"2021-03-04T00:00:00\", \"2021-03-07T00:00:00\", \"2021-03-08T00:00:00\", \"2021-03-09T00:00:00\", \"2021-03-10T00:00:00\", \"2021-03-11T00:00:00\", \"2021-03-14T00:00:00\", \"2021-03-15T00:00:00\", \"2021-03-16T00:00:00\", \"2021-03-18T00:00:00\", \"2021-03-21T00:00:00\", \"2021-03-22T00:00:00\", \"2021-03-23T00:00:00\", \"2021-03-24T00:00:00\", \"2021-03-25T00:00:00\", \"2021-03-28T00:00:00\", \"2021-03-29T00:00:00\", \"2021-03-31T00:00:00\", \"2021-04-01T00:00:00\", \"2021-04-04T00:00:00\", \"2021-04-05T00:00:00\", \"2021-04-06T00:00:00\", \"2021-04-07T00:00:00\", \"2021-04-08T00:00:00\", \"2021-04-11T00:00:00\", \"2021-04-12T00:00:00\", \"2021-04-13T00:00:00\", \"2021-04-15T00:00:00\", \"2021-04-18T00:00:00\", \"2021-04-19T00:00:00\", \"2021-04-20T00:00:00\", \"2021-04-21T00:00:00\", \"2021-04-22T00:00:00\", \"2021-04-25T00:00:00\", \"2021-04-26T00:00:00\", \"2021-04-27T00:00:00\", \"2021-04-28T00:00:00\", \"2021-04-29T00:00:00\", \"2021-05-02T00:00:00\", \"2021-05-03T00:00:00\", \"2021-05-04T00:00:00\", \"2021-05-05T00:00:00\", \"2021-05-06T00:00:00\", \"2021-05-09T00:00:00\", \"2021-05-11T00:00:00\", \"2021-05-12T00:00:00\", \"2021-05-16T00:00:00\", \"2021-05-17T00:00:00\", \"2021-05-18T00:00:00\", \"2021-05-19T00:00:00\", \"2021-05-20T00:00:00\", \"2021-05-23T00:00:00\", \"2021-05-24T00:00:00\", \"2021-05-25T00:00:00\", \"2021-05-27T00:00:00\", \"2021-05-30T00:00:00\", \"2021-05-31T00:00:00\", \"2021-06-01T00:00:00\", \"2021-06-02T00:00:00\", \"2021-06-03T00:00:00\", \"2021-06-06T00:00:00\", \"2021-06-07T00:00:00\", \"2021-06-08T00:00:00\", \"2021-06-09T00:00:00\", \"2021-06-10T00:00:00\", \"2021-06-13T00:00:00\", \"2021-06-14T00:00:00\", \"2021-06-15T00:00:00\", \"2021-06-16T00:00:00\", \"2021-06-17T00:00:00\", \"2021-06-20T00:00:00\", \"2021-06-21T00:00:00\", \"2021-06-22T00:00:00\", \"2021-06-23T00:00:00\", \"2021-06-24T00:00:00\", \"2021-06-27T00:00:00\", \"2021-06-28T00:00:00\", \"2021-06-29T00:00:00\", \"2021-06-30T00:00:00\", \"2021-07-01T00:00:00\", \"2021-07-04T00:00:00\", \"2021-07-05T00:00:00\", \"2021-07-06T00:00:00\", \"2021-07-07T00:00:00\", \"2021-07-08T00:00:00\", \"2021-07-11T00:00:00\"], \"y\": [null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, 254.0, 256.4, 255.2, 255.1, 254.9, 254.9, 254.8, 254.6, 254.0, 253.3, 252.5, 251.8, 251.5, 251.5, 251.9, 252.6, 253.3, 253.9, 254.3, 254.6, 254.7, 254.8, 254.8, 254.6, 254.4, 254.2, 253.9, 253.5, 253.2, 253.0]}],\n",
              "                        {\"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Future Value Forecast\"}, \"xaxis\": {\"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"title\": {\"text\": \"Price\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('d8c0c5ba-e7e2-4439-9cdd-564f35435fb2');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The Root Mean Square of the Model is: 5.608\n",
            "The Mean Absolute Percentage Error of the Model is: 1.799\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Forecast</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-05-31</th>\n",
              "      <td>254.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-01</th>\n",
              "      <td>256.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-02</th>\n",
              "      <td>255.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-03</th>\n",
              "      <td>255.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-06</th>\n",
              "      <td>254.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-07</th>\n",
              "      <td>254.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-08</th>\n",
              "      <td>254.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-09</th>\n",
              "      <td>254.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-10</th>\n",
              "      <td>254.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-13</th>\n",
              "      <td>253.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-14</th>\n",
              "      <td>252.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-15</th>\n",
              "      <td>251.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-16</th>\n",
              "      <td>251.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-17</th>\n",
              "      <td>251.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-20</th>\n",
              "      <td>251.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-21</th>\n",
              "      <td>252.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-22</th>\n",
              "      <td>253.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-23</th>\n",
              "      <td>253.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-24</th>\n",
              "      <td>254.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-27</th>\n",
              "      <td>254.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-28</th>\n",
              "      <td>254.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-29</th>\n",
              "      <td>254.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-30</th>\n",
              "      <td>254.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-01</th>\n",
              "      <td>254.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-04</th>\n",
              "      <td>254.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-05</th>\n",
              "      <td>254.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-06</th>\n",
              "      <td>253.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-07</th>\n",
              "      <td>253.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-08</th>\n",
              "      <td>253.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-11</th>\n",
              "      <td>253.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Forecast\n",
              "Date                \n",
              "2021-05-31     254.0\n",
              "2021-06-01     256.4\n",
              "2021-06-02     255.2\n",
              "2021-06-03     255.1\n",
              "2021-06-06     254.9\n",
              "2021-06-07     254.9\n",
              "2021-06-08     254.8\n",
              "2021-06-09     254.6\n",
              "2021-06-10     254.0\n",
              "2021-06-13     253.3\n",
              "2021-06-14     252.5\n",
              "2021-06-15     251.8\n",
              "2021-06-16     251.5\n",
              "2021-06-17     251.5\n",
              "2021-06-20     251.9\n",
              "2021-06-21     252.6\n",
              "2021-06-22     253.3\n",
              "2021-06-23     253.9\n",
              "2021-06-24     254.3\n",
              "2021-06-27     254.6\n",
              "2021-06-28     254.7\n",
              "2021-06-29     254.8\n",
              "2021-06-30     254.8\n",
              "2021-07-01     254.6\n",
              "2021-07-04     254.4\n",
              "2021-07-05     254.2\n",
              "2021-07-06     253.9\n",
              "2021-07-07     253.5\n",
              "2021-07-08     253.2\n",
              "2021-07-11     253.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    }
  ]
}