5. Model Comparison and Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from keras.models import load_model\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random \n",
    "import datetime as dt # working with time data\n",
    "import sklearn\n",
    "from sklearn import linear_model\n",
    "\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting colormap\n",
      "  Downloading colormap-1.0.4.tar.gz (17 kB)\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Building wheels for collected packages: colormap\n",
      "  Building wheel for colormap (setup.py): started\n",
      "  Building wheel for colormap (setup.py): finished with status 'done'\n",
      "  Created wheel for colormap: filename=colormap-1.0.4-py3-none-any.whl size=15631 sha256=5b7e2159d827e85ee3776505d91e8d9215a297c1ddf7bb2c04e2664b53ceb006\n",
      "  Stored in directory: c:\\users\\sidizainulabideen.a\\appdata\\local\\pip\\cache\\wheels\\bb\\25\\18\\6b007d77ac1220e0530efa2af14569bb803d77f140366fb313\n",
      "Successfully built colormap\n",
      "Installing collected packages: colormap\n",
      "Successfully installed colormap-1.0.4\n",
      "\n",
      "[notice] A new release of pip available: 22.2 -> 22.2.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "# !pip install colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\sidizainulabideen.a\\.conda\\envs\\SidiEnv\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator Lasso from version 1.1.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "c:\\Users\\sidizainulabideen.a\\.conda\\envs\\SidiEnv\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator LinearRegression from version 1.1.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n",
      "c:\\Users\\sidizainulabideen.a\\.conda\\envs\\SidiEnv\\lib\\site-packages\\sklearn\\base.py:329: UserWarning: Trying to unpickle estimator Ridge from version 1.1.1 when using version 1.0.2. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:\n",
      "https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "path = \"../Models/\"\n",
    "\n",
    "arimax = pickle.load(open(path+\"arimax.sav\", 'rb'))\n",
    "\n",
    "RNN_base = load_model(path+'RNN_base.h5')\n",
    "RNN_Opt_D_ES = load_model(path+'RNN_optimized_dropout_es.h5')\n",
    "RNN_Opt = load_model(path+'RNN_optimized.h5')\n",
    "RNN_Opt_D = load_model(path+'RNN_optimized_es.h5')\n",
    "\n",
    "LSTM_base = load_model(path+'LSTM_base.h5')\n",
    "LSTM_Opt_D_ES = load_model(path+'LSTM_optimized_dropout_es.h5')\n",
    "LSTM_Opt = load_model(path+'LSTM_optimized.h5')\n",
    "LSTM_Opt_D = load_model(path+'LSTM_optimized_es.h5')\n",
    "\n",
    "GRU_base = load_model(path+'GRU_base.h5')\n",
    "GRU_Opt_D_ES = load_model(path+'GRU_optimized_dropout_es.h5')\n",
    "GRU_Opt = load_model(path+'GRU_optimized.h5')\n",
    "GRU_Opt_D = load_model(path+'GRU_optimized_es.h5')\n",
    "\n",
    "ML_Lasso = pickle.load(open(path+'Lasso_Regression.sav','rb'))\n",
    "ML_Linear = pickle.load(open(path+'Linear_Regression.sav','rb'))\n",
    "ML_Ridge = pickle.load(open(path+'Ridge_Regression.sav','rb'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the pre-processed dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('..\\Data\\gasoline_price_sentiment_pre-processed_7.xlsx', 'Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index(keys='Date', drop= True, inplace= True)\n",
    "df.drop('Unnamed: 0', inplace= True, axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['price', 'popularity', 'general_sentiment', 'positive_outlook',\n",
       "       'happiness', 'bittersweet', 'confidence', 'disgust', 'disagreement',\n",
       "       'anxiety', 'gloominess', 'distress', 'violent', 'unexpectedness',\n",
       "       'timeliness', 'confusion', 'feelingsVsTruth', 'buyVsSell',\n",
       "       'buyVsSellForecast', 'priceTrend', 'pricePrediction', 'volatile',\n",
       "       'consumptionCommodity', 'productionCommodity', 'regulatory',\n",
       "       'supplyDemandBalance', 'supplyDemandBalanceForecast', 'exploration',\n",
       "       'accidents', 'National_Holiday', 'Weekend', 'Holidays', 'happiness_Log',\n",
       "       'bittersweet_Log', 'disgust_Log', 'disagreement_Log', 'anxiety_Log',\n",
       "       'gloominess_Log', 'distress_Log', 'violent_Log', 'unexpectedness_Log',\n",
       "       'confusion_Log', 'buyVsSell_sqrt', 'pricePrediction_Log',\n",
       "       'volatile_Log', 'productionCommodity_Log', 'regulatory_Log',\n",
       "       'supplyDemandBalance_Log', 'exploration_Log', 'accidents_Log',\n",
       "       'month-1', 'month-2', 'month-3', 'month-4', 'month-5', 'month-6',\n",
       "       'month-7', 'month-8', 'month-9', 'month-10', 'month-11', 'month-12',\n",
       "       'dayofweek-0', 'dayofweek-1', 'dayofweek-2', 'dayofweek-3',\n",
       "       'dayofweek-4', 'dayofweek-5', 'dayofweek-6', 'weekend', 'sunday',\n",
       "       'price_lag_7', 'price_rm', 'price_ewm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_variable_columns = ['price', 'happiness_Log', 'bittersweet_Log',\n",
    "       'disgust_Log', 'disagreement_Log', 'anxiety_Log', 'gloominess_Log',\n",
    "       'distress_Log', 'violent_Log', 'unexpectedness_Log', 'confusion_Log',\n",
    "       'buyVsSell_sqrt', 'pricePrediction_Log', 'volatile_Log',\n",
    "       'productionCommodity_Log', 'regulatory_Log', 'supplyDemandBalance_Log',\n",
    "       'exploration_Log', 'accidents_Log', 'supplyDemandBalanceForecast', \n",
    "       'consumptionCommodity', 'priceTrend', 'buyVsSellForecast', 'feelingsVsTruth',\n",
    "       'timeliness', 'confidence', 'popularity', 'general_sentiment', 'positive_outlook',\n",
    "       'price_lag_7', 'price_rm', 'price_ewm', 'month-1', 'month-2', \n",
    "       'month-3', 'month-4', 'month-5', 'month-6', 'month-7', 'month-8', \n",
    "       'month-9', 'month-10', 'month-11', 'month-12', 'dayofweek-0',\n",
    "       'dayofweek-1', 'dayofweek-2', 'dayofweek-3', 'dayofweek-4',\n",
    "       'dayofweek-5', 'dayofweek-6', 'weekend', 'sunday']\n",
    "\n",
    "selected_variable_columns_ML = ['happiness_Log', 'bittersweet_Log',\n",
    "       'disgust_Log', 'disagreement_Log', 'anxiety_Log', 'gloominess_Log',\n",
    "       'distress_Log', 'violent_Log', 'unexpectedness_Log', 'confusion_Log',\n",
    "       'buyVsSell_sqrt', 'pricePrediction_Log', 'volatile_Log',\n",
    "       'productionCommodity_Log', 'regulatory_Log', 'supplyDemandBalance_Log',\n",
    "       'exploration_Log', 'accidents_Log', 'supplyDemandBalanceForecast', \n",
    "       'consumptionCommodity', 'priceTrend', 'buyVsSellForecast', 'feelingsVsTruth',\n",
    "       'timeliness', 'confidence', 'popularity', 'general_sentiment', 'positive_outlook', 'month-1', 'month-2', \n",
    "       'month-3', 'month-4', 'month-5', 'month-6', 'month-7', 'month-8', \n",
    "       'month-9', 'month-10', 'month-11', 'month-12', 'dayofweek-0',\n",
    "       'dayofweek-1', 'dayofweek-2', 'dayofweek-3', 'dayofweek-4',\n",
    "       'dayofweek-5', 'dayofweek-6', 'weekend', 'sunday']\n",
    "#We are taking the transformed columns and the derived features\n",
    "\n",
    "df_subset = df[selected_variable_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train-test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_start = '2010-01-08'\n",
    "te_start = '2017-04-01' \n",
    "te_end = '2018-12-31'\n",
    "tr_end = '2017-03-31'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [],
   "source": [
    "def splitdataset(df_subset):\n",
    "    tr_start = '2010-01-08'\n",
    "    te_start = '2017-04-01' \n",
    "    te_end = '2018-12-31'\n",
    "    tr_end = '2017-03-31'\n",
    "\n",
    "    train = df_subset[tr_start : tr_end]\n",
    "    test = df_subset[te_start : te_end]\n",
    "\n",
    "    return (train,test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "def splitdatasetML(xlagged,y):\n",
    "    tr_start = '2010-01-08'\n",
    "    te_start = '2017-04-01' \n",
    "    te_end = '2018-12-31'\n",
    "    tr_end = '2017-03-31'\n",
    "\n",
    "    x_train = xlagged[tr_start:tr_end]\n",
    "\n",
    "    x_test = xlagged[te_start:te_end]\n",
    "\n",
    "    y_train = y[tr_start:tr_end]\n",
    "\n",
    "    y_test = y[te_start:te_end]\n",
    "\n",
    "    return (x_test,x_train,y_train,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [],
   "source": [
    "train,test = splitdataset(df_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_lagged_features(df, col, lag_number = 7):\n",
    "    \n",
    "    df[col]=df[col].shift(lag_number)\n",
    "    \n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\829231875.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  X_ML.drop(['price_lag_7', 'price_rm', 'price_ewm'], inplace= True, axis = 1)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\446963715.py:3: SettingWithCopyWarning: \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",
      "  df[col]=df[col].shift(lag_number)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3287, 50)\n"
     ]
    }
   ],
   "source": [
    "X_ML = df[selected_variable_columns]\n",
    "\n",
    "engineered_features = X_ML[['price_lag_7', 'price_rm', 'price_ewm']]\n",
    "\n",
    "\n",
    "\n",
    "X_ML.drop(['price_lag_7', 'price_rm', 'price_ewm'], inplace= True, axis = 1)\n",
    "\n",
    "print(X_ML.shape)\n",
    "\n",
    "ML_X = create_lagged_features(X_ML, selected_variable_columns_ML, lag_number = 7)\n",
    "\n",
    "ML_X = pd.concat([ML_X, engineered_features], axis = 1)\n",
    "\n",
    "ML_X.drop(['price'], inplace= True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [],
   "source": [
    "ML_X = ML_X.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3280, 52)"
      ]
     },
     "execution_count": 486,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test,x_train,y_train,y_test = splitdatasetML(ML_X,df_subset['price'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardization of Independent Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Standardization_Dataset(train,test):\n",
    "\n",
    "    x_scaler = StandardScaler()\n",
    "    x_scaled = x_scaler.fit_transform(train.iloc[:, 1:])\n",
    "    y = np.reshape(np.array(train.iloc[:,0]), (-1,1))\n",
    "    train_scaled = np.hstack((x_scaled, y))\n",
    "    x_scaled = x_scaler.fit_transform(test.iloc[:, 1:])\n",
    "    y = np.reshape(np.array(test.iloc[:,0]), (-1,1))\n",
    "    test_scaled = np.hstack((x_scaled, y))\n",
    "\n",
    "    return (train_scaled, test_scaled,x_scaler)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Standardization_Dataset_ML(MLtestLagged_X,x_scaler):\n",
    "\n",
    "    x_scaled = x_scaler.fit_transform(MLtestLagged_X)\n",
    "\n",
    "    return (x_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scaled, test_scaled,x_scaler = Standardization_Dataset(train,test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [],
   "source": [
    "MLtestLagged_X_Scaled= Standardization_Dataset_ML(x_test,x_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(640, 52)"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLtestLagged_X_Scaled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing dataset for modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Dataset Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def create_lagged_features(df, col, lag_number = 7):\n",
    "    \n",
    "#     df[col]=df[col].shift(lag_number)\n",
    "    \n",
    "#     return(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining a function to split multivariate sequences into samples\n",
    "def split_sequences(sequences, n_steps):\n",
    "\tX, y = list(), list()\n",
    "\tfor i in range(len(sequences)):\n",
    "\t\t# find the end of this pattern\n",
    "\t\tend_ix = i + n_steps\n",
    "\t\t# check if we are beyond the dataset\n",
    "\t\tif end_ix > len(sequences):\n",
    "\t\t\tbreak\n",
    "\t\t# gather input and output parts of the pattern\n",
    "\t\tseq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1]\n",
    "\t\tX.append(seq_x)\n",
    "\t\ty.append(seq_y)\n",
    "\treturn np.array(X), np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Learning lagged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_trainDL, y_testDLDL = split_sequences(train_scaled, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_testDL, y_testDL = split_sequences(test_scaled, 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(634, 7, 52)\n",
      "(634,)\n"
     ]
    }
   ],
   "source": [
    "print(x_testDL.shape)\n",
    "print(y_testDL.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Machine Learning Lagged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LinearRegression\n",
    "# MLtestLagged_X_Scaled "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MLtestLagged_X_Scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pred_lrTest = pd.Series(reg.predict(MLtestLagged_X_Scaled), index = y_test[7:].index)\n",
    "# print(\"MSE for Train :\",mean_squared_error(y_test[7:], pred_lrTest, squared=False))\n",
    "# print(\"RMSE for Train :\",np.sqrt(mean_squared_error(y_test[7:], pred_lrTest, squared=True)))\n",
    "# print(\"MAPE for Train :\",mean_absolute_percentage_error(y_test[7:], pred_lrTest))\n",
    "\n",
    "# print('Training score :', mean_absolute_percentage_error(y_test[7:], pred_lrTest))\n",
    "\n",
    "# y_train.plot(label='true', figsize=(10,6))\n",
    "# pred_lrTrain.plot(label='linear_regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model-building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set seed to ensure reproducibility\n",
    "def set_seed(seed: int):\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    tf.random.set_seed(seed)\n",
    "\n",
    "#Setting seed value as 7\n",
    "set_seed(7)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Comparison\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(640, 52)\n",
      "ARIMAX model MSE:0.4808468965137774\n",
      "The MSE for the ARIMAX model model is: 0.48\n",
      "The RMSE for the ARIMAX model model is: 0.69\n",
      "The MAPE for the ARIMAX model model is 0.01\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "exog_features = df_subset.drop(axis=1, columns='price').columns\n",
    "exog_test = df_subset[te_start : te_end].loc[:, exog_features]\n",
    "tes = df_subset[te_start : te_end].loc[:, 'price']\n",
    "\n",
    "print(exog_test.shape)\n",
    "\n",
    "st = str(exog_test.reset_index().iloc[0, 0])[:10]\n",
    "en = str(exog_test.reset_index().iloc[-1, 0])[:10]\n",
    "\n",
    "\n",
    "\n",
    "ArimaxPred = arimax.predict(st, en, exog = exog_test)\n",
    "print('ARIMAX model MSE:{}'.format(mean_squared_error(tes,ArimaxPred)))\n",
    "print('The MSE for the ARIMAX model model is: {0:.2f}'.format(mean_squared_error(tes,ArimaxPred)))\n",
    "print('The RMSE for the ARIMAX model model is: {0:.2f}'.format(np.sqrt(mean_squared_error(tes,ArimaxPred))))\n",
    "print('The MAPE for the ARIMAX model model is {0:.2f}'.format(mean_absolute_percentage_error(tes,ArimaxPred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statsmodels.tsa.statespace.sarimax.SARIMAXResultsWrapper"
      ]
     },
     "execution_count": 503,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arimax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-04-01    66.419143\n",
       "2017-04-02    66.629836\n",
       "2017-04-03    67.033609\n",
       "2017-04-04    66.590841\n",
       "2017-04-05    67.459957\n",
       "                ...    \n",
       "2018-12-27    56.121542\n",
       "2018-12-28    56.369570\n",
       "2018-12-29    55.957982\n",
       "2018-12-30    55.925397\n",
       "2018-12-31    55.856314\n",
       "Freq: D, Name: predicted_mean, Length: 640, dtype: float64"
      ]
     },
     "execution_count": 504,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ArimaxPred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [],
   "source": [
    "ArimaxPred.index = ArimaxPred.index.to_timestamp()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-04-01    66.419143\n",
       "2017-04-02    66.629836\n",
       "2017-04-03    67.033609\n",
       "2017-04-04    66.590841\n",
       "2017-04-05    67.459957\n",
       "                ...    \n",
       "2018-12-27    56.121542\n",
       "2018-12-28    56.369570\n",
       "2018-12-29    55.957982\n",
       "2018-12-30    55.925397\n",
       "2018-12-31    55.856314\n",
       "Freq: D, Name: predicted_mean, Length: 640, dtype: float64"
      ]
     },
     "execution_count": 506,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ArimaxPred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 7ms/step\n",
      "The MSE for the baseline RNN model is: 803.47\n",
      "The RMSE for the baseline RNN model is: 28.35\n",
      "The MAPE for the baseline RNN model is 0.29\n"
     ]
    }
   ],
   "source": [
    "pred_RNN = RNN_base.predict(x_testDL)\n",
    "print('The MSE for the baseline RNN model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_RNN)))\n",
    "print('The RMSE for the baseline RNN model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_RNN))))\n",
    "print('The MAPE for the baseline RNN model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_RNN)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 6ms/step\n",
      "The MSE for the Opt RNN model is: 768.88\n",
      "The RMSE for the Opt RNN model is: 27.73\n",
      "The MAPE for the Opt RNN model is 0.28\n"
     ]
    }
   ],
   "source": [
    "pred_RNN_Opt = RNN_Opt.predict(x_testDL)\n",
    "print('The MSE for the Opt RNN model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_RNN_Opt)))\n",
    "print('The RMSE for the Opt RNN model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_RNN_Opt))))\n",
    "print('The MAPE for the Opt RNN model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_RNN_Opt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 6ms/step\n",
      "The MSE for the Opt Dropout RNN model is: 801.39\n",
      "The RMSE for the Opt Dropout RNN model is: 28.31\n",
      "The MAPE for the Opt Dropout RNN model is 0.29\n"
     ]
    }
   ],
   "source": [
    "pred_RNN_Opt_D = RNN_Opt_D.predict(x_testDL)\n",
    "print('The MSE for the Opt Dropout RNN model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_RNN_Opt_D)))\n",
    "print('The RMSE for the Opt Dropout RNN model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_RNN_Opt_D))))\n",
    "print('The MAPE for the Opt Dropout RNN model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_RNN_Opt_D)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 5ms/step\n",
      "The MSE for the Opt Dropout RNN Early Stop model is: 282.41\n",
      "The RMSE for the Opt Dropout RNN Early Stop model is: 16.81\n",
      "The MAPE for the Opt Dropout RNN Early Stop model is 0.16\n"
     ]
    }
   ],
   "source": [
    "pred_RNN_Opt_D_ES = RNN_Opt_D_ES.predict(x_testDL)\n",
    "print('The MSE for the Opt Dropout RNN Early Stop model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_RNN_Opt_D_ES)))\n",
    "print('The RMSE for the Opt Dropout RNN Early Stop model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_RNN_Opt_D_ES))))\n",
    "print('The MAPE for the Opt Dropout RNN Early Stop model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_RNN_Opt_D_ES)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 7ms/step\n",
      "The MSE for the baseline LSTM model is: 841.37\n",
      "The RMSE for the baseline LSTM model is: 29.01\n",
      "The MAPE for the baseline LSTM model is 0.30\n"
     ]
    }
   ],
   "source": [
    "pred_LSTM = LSTM_base.predict(x_testDL)\n",
    "print('The MSE for the baseline LSTM model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_LSTM)))\n",
    "print('The RMSE for the baseline LSTM model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_LSTM))))\n",
    "print('The MAPE for the baseline LSTM model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_LSTM)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 8ms/step\n",
      "The MSE for the Opt LSTM model is: 923.34\n",
      "The RMSE for the Opt LSTM model is: 30.39\n",
      "The MAPE for the Opt LSTM model is 0.32\n"
     ]
    }
   ],
   "source": [
    "pred_LSTM_Opt = LSTM_Opt.predict(x_testDL)\n",
    "print('The MSE for the Opt LSTM model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_LSTM_Opt)))\n",
    "print('The RMSE for the Opt LSTM model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_LSTM_Opt))))\n",
    "print('The MAPE for the Opt LSTM model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_LSTM_Opt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 8ms/step\n",
      "The MSE for the Opt Dropout LSTM model is: 575.41\n",
      "The RMSE for the Opt Dropout LSTM model is: 23.99\n",
      "The MAPE for the Opt Dropout LSTM model is 0.25\n"
     ]
    }
   ],
   "source": [
    "pred_LSTM_Opt_D = LSTM_Opt_D.predict(x_testDL)\n",
    "print('The MSE for the Opt Dropout LSTM model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_LSTM_Opt_D)))\n",
    "print('The RMSE for the Opt Dropout LSTM model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_LSTM_Opt_D))))\n",
    "print('The MAPE for the Opt Dropout LSTM model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_LSTM_Opt_D)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 7ms/step\n",
      "The MSE for the Opt Dropout LSTM Early Stop model is: 557.81\n",
      "The RMSE for the Opt Dropout LSTM Early Stop model is: 23.62\n",
      "The MAPE for the Opt Dropout RLSTM Early Stop model is 0.24\n"
     ]
    }
   ],
   "source": [
    "pred_LSTM_Opt_D_ES = LSTM_Opt_D_ES.predict(x_testDL)\n",
    "print('The MSE for the Opt Dropout LSTM Early Stop model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_LSTM_Opt_D_ES)))\n",
    "print('The RMSE for the Opt Dropout LSTM Early Stop model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_LSTM_Opt_D_ES))))\n",
    "print('The MAPE for the Opt Dropout RLSTM Early Stop model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_LSTM_Opt_D_ES)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 7ms/step\n",
      "The MSE for the baseline GRU model is: 792.93\n",
      "The RMSE for the baseline GRU model is: 28.16\n",
      "The MAPE for the baseline GRU model is 0.29\n"
     ]
    }
   ],
   "source": [
    "pred_GRU = GRU_base.predict(x_testDL)\n",
    "print('The MSE for the baseline GRU model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_GRU)))\n",
    "print('The RMSE for the baseline GRU model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_GRU))))\n",
    "print('The MAPE for the baseline GRU model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_GRU)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 6ms/step\n",
      "The MSE for the Opt GRU model is: 854.53\n",
      "The RMSE for the Opt GRU model is: 29.23\n",
      "The MAPE for the Opt GRU model is 0.31\n"
     ]
    }
   ],
   "source": [
    "pred_GRU_Opt = GRU_Opt.predict(x_testDL)\n",
    "print('The MSE for the Opt GRU model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_GRU_Opt)))\n",
    "print('The RMSE for the Opt GRU model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_GRU_Opt))))\n",
    "print('The MAPE for the Opt GRU model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_GRU_Opt)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 7ms/step\n",
      "The MSE for the Opt Dropout GRU model is: 749.62\n",
      "The RMSE for the Opt Dropout GRU model is: 27.38\n",
      "The MAPE for the Opt Dropout GRU model is 0.28\n"
     ]
    }
   ],
   "source": [
    "pred_GRU_Opt_D = GRU_Opt_D.predict(x_testDL)\n",
    "print('The MSE for the Opt Dropout GRU model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_GRU_Opt_D)))\n",
    "print('The RMSE for the Opt Dropout GRU model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_GRU_Opt_D))))\n",
    "print('The MAPE for the Opt Dropout GRU model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_GRU_Opt_D)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 6ms/step\n",
      "The MSE for the Opt Dropout GRU Early Stop model is: 472.60\n",
      "The RMSE for the Opt Dropout GRU Early Stop model is: 21.74\n",
      "The MAPE for the Opt Dropout GRU Early Stop model is 0.22\n"
     ]
    }
   ],
   "source": [
    "pred_GRU_Opt_D_ES = GRU_Opt_D_ES.predict(x_testDL)\n",
    "print('The MSE for the Opt Dropout GRU Early Stop model is: {0:.2f}'.format(mean_squared_error(y_testDL, pred_GRU_Opt_D_ES)))\n",
    "print('The RMSE for the Opt Dropout GRU Early Stop model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_testDL, pred_GRU_Opt_D_ES))))\n",
    "print('The MAPE for the Opt Dropout GRU Early Stop model is {0:.2f}'.format(mean_absolute_percentage_error(y_testDL, pred_GRU_Opt_D_ES)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE for the Lasso model is: 806.41\n",
      "The RMSE for the Lasso model is: 28.40\n",
      "The MAPE for the Lasso model is 0.30\n"
     ]
    }
   ],
   "source": [
    "pred_Lasso = pd.Series(ML_Lasso.predict(MLtestLagged_X_Scaled),index = y_test.index)\n",
    "print('The MSE for the Lasso model is: {0:.2f}'.format(mean_squared_error(y_test, pred_Lasso)))\n",
    "print('The RMSE for the Lasso model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, pred_Lasso))))\n",
    "print('The MAPE for the Lasso model is {0:.2f}'.format(mean_absolute_percentage_error(y_test, pred_Lasso)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE for the Ridge model is: 803.45\n",
      "The RMSE for the Ridge model is: 28.35\n",
      "The MAPE for the Ridge model is 0.30\n"
     ]
    }
   ],
   "source": [
    "pred_Ridge = pd.Series(ML_Ridge.predict(MLtestLagged_X_Scaled),index = y_test.index)\n",
    "print('The MSE for the Ridge model is: {0:.2f}'.format(mean_squared_error(y_test, pred_Ridge)))\n",
    "print('The RMSE for the Ridge model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, pred_Ridge))))\n",
    "print('The MAPE for the Ridge model is {0:.2f}'.format(mean_absolute_percentage_error(y_test, pred_Ridge)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The MSE for the Ridge model is: 806.12\n",
      "The RMSE for the Ridge model is: 28.39\n",
      "The MAPE for the Ridge model is 0.30\n"
     ]
    }
   ],
   "source": [
    "pred_Linear = pd.Series(ML_Linear.predict(MLtestLagged_X_Scaled),index = y_test.index)\n",
    "print('The MSE for the Ridge model is: {0:.2f}'.format(mean_squared_error(y_test, pred_Linear)))\n",
    "print('The RMSE for the Ridge model is: {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, pred_Linear))))\n",
    "print('The MAPE for the Ridge model is {0:.2f}'.format(mean_absolute_percentage_error(y_test, pred_Linear)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [],
   "source": [
    "ArimaxMSE=(mean_squared_error(tes,ArimaxPred))\n",
    "ArimaxRMSE=np.sqrt(mean_squared_error(tes,ArimaxPred))\n",
    "ArimaxMAPE=(mean_absolute_percentage_error(tes,ArimaxPred))\n",
    "\n",
    "RNNMSE=(mean_squared_error(y_testDL, pred_RNN))\n",
    "RNNRMSE=(np.sqrt(mean_squared_error(y_testDL, pred_RNN)))\n",
    "RNNMAPE=(mean_absolute_percentage_error(y_testDL, pred_RNN))\n",
    "\n",
    "OPTRNNMSE=(mean_squared_error(y_testDL, pred_RNN_Opt))\n",
    "OPTRNNRMSE=(np.sqrt(mean_squared_error(y_testDL, pred_RNN_Opt)))\n",
    "OPTRNNMAPE=(mean_absolute_percentage_error(y_testDL, pred_RNN_Opt))\n",
    "\n",
    "OPT_D_RNNMSE=(mean_squared_error(y_testDL, pred_RNN_Opt_D))\n",
    "OPT_D_RNNRMSE=(np.sqrt(mean_squared_error(y_testDL, pred_RNN_Opt_D)))\n",
    "OPT_D_RNNMAPE=(mean_absolute_percentage_error(y_testDL, pred_RNN_Opt_D))\n",
    "\n",
    "OPT_D_ES_RNNMSE=(mean_squared_error(y_testDL, pred_RNN_Opt_D_ES))\n",
    "OPT_D_ES_RNNRMSE=(np.sqrt(mean_squared_error(y_testDL, pred_RNN_Opt_D_ES)))\n",
    "OPT_D_ES_RNNMAPE=(mean_absolute_percentage_error(y_testDL, pred_RNN_Opt_D_ES))\n",
    "\n",
    "LSTMMSE=(mean_squared_error(y_testDL, pred_LSTM))\n",
    "LSTMRMSE=(np.sqrt(mean_squared_error(y_testDL, pred_LSTM)))\n",
    "LSTMMAPE=(mean_absolute_percentage_error(y_testDL, pred_LSTM))\n",
    "\n",
    "OPTLSTMMSE=(mean_squared_error(y_testDL, pred_LSTM_Opt))\n",
    "OPTLSTMRMSE=(np.sqrt(mean_squared_error(y_testDL, pred_LSTM_Opt)))\n",
    "OPTLSTMMAPE=(mean_absolute_percentage_error(y_testDL, pred_LSTM_Opt))\n",
    "\n",
    "OPT_D_LSTMMSE=(mean_squared_error(y_testDL, pred_LSTM_Opt_D))\n",
    "OPT_D_LSTMRMSE=(np.sqrt(mean_squared_error(y_testDL, pred_LSTM_Opt_D)))\n",
    "OPT_D_LSTMMAPE=(mean_absolute_percentage_error(y_testDL, pred_LSTM_Opt_D))\n",
    "\n",
    "OPT_D_ES_LSTMMSE=(mean_squared_error(y_testDL, pred_LSTM_Opt_D_ES))\n",
    "OPT_D_ES_LSTMRMSE=(np.sqrt(mean_squared_error(y_testDL, pred_LSTM_Opt_D_ES)))\n",
    "OPT_D_ES_LSTMMAPE=(mean_absolute_percentage_error(y_testDL, pred_LSTM_Opt_D_ES))\n",
    "\n",
    "GRUMSE=(mean_squared_error(y_testDL, pred_GRU))\n",
    "GRURMSE=(np.sqrt(mean_squared_error(y_testDL, pred_GRU)))\n",
    "GRUMAPE=(mean_absolute_percentage_error(y_testDL, pred_GRU))\n",
    "\n",
    "OPTGRUMSE=(mean_squared_error(y_testDL, pred_GRU_Opt))\n",
    "OPTGRURMSE=(np.sqrt(mean_squared_error(y_testDL, pred_GRU_Opt)))\n",
    "OPTGRUMAPE=(mean_absolute_percentage_error(y_testDL, pred_GRU_Opt))\n",
    "\n",
    "OPT_D_GRUMSE=(mean_squared_error(y_testDL, pred_GRU_Opt_D))\n",
    "OPT_D_GRURMSE=(np.sqrt(mean_squared_error(y_testDL, pred_GRU_Opt_D)))\n",
    "OPT_D_GRUMAPE=(mean_absolute_percentage_error(y_testDL, pred_GRU_Opt_D))\n",
    "\n",
    "OPT_D_ES_GRUMSE=(mean_squared_error(y_testDL, pred_GRU_Opt_D_ES))\n",
    "OPT_D_ES_GRURMSE=(np.sqrt(mean_squared_error(y_testDL, pred_GRU_Opt_D_ES)))\n",
    "OPT_D_ES_GRUMAPE=(mean_absolute_percentage_error(y_testDL, pred_GRU_Opt_D_ES))\n",
    "\n",
    "LassoMSE=mean_squared_error(y_test, pred_Lasso)\n",
    "LassoRMSE=(np.sqrt(mean_squared_error(y_test, pred_Lasso)))\n",
    "LassoMAPE=(mean_absolute_percentage_error(y_test, pred_Lasso))\n",
    "\n",
    "RidgeMSE=(mean_squared_error(y_test, pred_Ridge))\n",
    "RidgeRMSE=(np.sqrt(mean_squared_error(y_test, pred_Ridge)))\n",
    "RidgeMAPE=(mean_absolute_percentage_error(y_test, pred_Ridge))\n",
    "\n",
    "LinearMSE=(mean_squared_error(y_test, pred_Linear))\n",
    "LinearRMSE=(np.sqrt(mean_squared_error(y_test, pred_Linear)))\n",
    "LinearMAPE=(mean_absolute_percentage_error(y_test, pred_Linear))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_Evaluation_Rank_Base(GR_Result):\n",
    "\n",
    "    Gr_Df=GR_Result\n",
    "\n",
    "    for i in Gr_Df.index:\n",
    "        check = 0\n",
    "\n",
    "        sum=Gr_Df['Rank MSE'][i]+Gr_Df['Rank RMSE'][i]+Gr_Df['Rank MAPE'][i]\n",
    "\n",
    "        if sum == 3:\n",
    "            check = 1\n",
    "            ModelName=Gr_Df['Model'][i]\n",
    "            print(\"Model of {}\".format(ModelName) + \" achieve all rank 1 for metrics evaluation.\")\n",
    "            print(\"Suggest using {}\".format(ModelName))\n",
    "\n",
    "            break\n",
    "            \n",
    "\n",
    "\n",
    "    if check == 0:\n",
    "        ModelName=Gr_Df.loc[(Gr_Df['Rank MAPE'] == 1), 'Model'].iloc[0]\n",
    "        print(\"Model of {}\".format(ModelName) + \" achieve rank 1 for MAPE.\")\n",
    "        print(\"Suggest to check the dataframe and plot comparison on test set\")\n",
    "        Gr_Df\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:4: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:14: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:21: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:24: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:27: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:30: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:33: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:39: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:42: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:46: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:49: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model of Arima achieve all rank 1 for metrics evaluation.\n",
      "Suggest using Arima\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\920585166.py:52: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "ModelComparison = pd.DataFrame(columns=['Model','MSE','RMSE','MAPE'])\n",
    "\n",
    "new_row = {'Model':'Arima', 'MSE':ArimaxMSE, 'RMSE':ArimaxRMSE, 'MAPE':ArimaxMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "\n",
    "new_row = {'Model':'RNN', 'MSE':RNNMSE, 'RMSE':RNNRMSE, 'MAPE':RNNMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'RNN_OPT', 'MSE':OPTRNNMSE, 'RMSE':OPTRNNRMSE, 'MAPE':OPTRNNMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'RNN_OPT_D', 'MSE':OPT_D_RNNMSE, 'RMSE':OPT_D_RNNRMSE, 'MAPE':OPT_D_RNNMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'RNN_OPT_D_ES', 'MSE':OPT_D_ES_RNNMSE, 'RMSE':OPT_D_ES_RNNRMSE, 'MAPE':OPT_D_ES_RNNMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "\n",
    "new_row = {'Model':'GRU', 'MSE':GRUMSE, 'RMSE':GRURMSE, 'MAPE':GRUMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'GRU_OPT', 'MSE':OPTGRUMSE, 'RMSE':OPTGRURMSE, 'MAPE':OPTGRUMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'GRU_OPT_D', 'MSE':OPT_D_GRUMSE, 'RMSE':OPT_D_GRURMSE, 'MAPE':OPT_D_GRUMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'GRU_OPT_D_ES', 'MSE':OPT_D_ES_GRUMSE, 'RMSE':OPT_D_ES_GRURMSE, 'MAPE':OPT_D_ES_GRUMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'LSTM', 'MSE':LSTMMSE, 'RMSE':LSTMRMSE, 'MAPE':LSTMMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'LSTM_OPT', 'MSE':OPTLSTMMSE, 'RMSE':OPTLSTMRMSE, 'MAPE':OPTLSTMMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'LSTM_OPT_D', 'MSE':OPT_D_LSTMMSE, 'RMSE':OPT_D_LSTMRMSE, 'MAPE':OPT_D_LSTMMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'LSTM_OPT_D_ES', 'MSE':OPT_D_ES_LSTMMSE, 'RMSE':OPT_D_ES_LSTMRMSE, 'MAPE':OPT_D_ES_LSTMMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "\n",
    "new_row = {'Model':'Lasso', 'MSE':LassoMSE, 'RMSE':LassoRMSE, 'MAPE':LassoMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'Ridge', 'MSE':RidgeMSE, 'RMSE':RidgeRMSE, 'MAPE':RidgeMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "new_row = {'Model':'Linear', 'MSE':LinearMSE, 'RMSE':LinearRMSE, 'MAPE':LinearMAPE}\n",
    "ModelComparison = ModelComparison.append(new_row, ignore_index=True)\n",
    "\n",
    "ModelComparison['Rank MSE'] = ModelComparison['MSE'].rank(ascending = True)\n",
    "ModelComparison['Rank RMSE'] = ModelComparison['RMSE'].rank(ascending = True)\n",
    "ModelComparison['Rank MAPE'] = ModelComparison['MAPE'].rank(ascending = True)\n",
    "\n",
    "Model_Evaluation_Rank_Base(ModelComparison)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [
    {
     "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>Model</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>MAPE</th>\n",
       "      <th>Rank MSE</th>\n",
       "      <th>Rank RMSE</th>\n",
       "      <th>Rank MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arima</td>\n",
       "      <td>0.480847</td>\n",
       "      <td>0.693431</td>\n",
       "      <td>0.006893</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RNN</td>\n",
       "      <td>803.471095</td>\n",
       "      <td>28.345566</td>\n",
       "      <td>0.292678</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RNN_OPT</td>\n",
       "      <td>768.881366</td>\n",
       "      <td>27.72871</td>\n",
       "      <td>0.283834</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RNN_OPT_D</td>\n",
       "      <td>801.385703</td>\n",
       "      <td>28.308757</td>\n",
       "      <td>0.287185</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RNN_OPT_D_ES</td>\n",
       "      <td>282.412011</td>\n",
       "      <td>16.805119</td>\n",
       "      <td>0.160293</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GRU</td>\n",
       "      <td>792.926785</td>\n",
       "      <td>28.158956</td>\n",
       "      <td>0.288617</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GRU_OPT</td>\n",
       "      <td>854.533657</td>\n",
       "      <td>29.232408</td>\n",
       "      <td>0.307432</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GRU_OPT_D</td>\n",
       "      <td>749.618105</td>\n",
       "      <td>27.379155</td>\n",
       "      <td>0.284273</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GRU_OPT_D_ES</td>\n",
       "      <td>472.601374</td>\n",
       "      <td>21.739397</td>\n",
       "      <td>0.220161</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>841.367953</td>\n",
       "      <td>29.006343</td>\n",
       "      <td>0.29836</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LSTM_OPT</td>\n",
       "      <td>923.344036</td>\n",
       "      <td>30.386577</td>\n",
       "      <td>0.323354</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LSTM_OPT_D</td>\n",
       "      <td>575.405737</td>\n",
       "      <td>23.987616</td>\n",
       "      <td>0.247318</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LSTM_OPT_D_ES</td>\n",
       "      <td>557.814133</td>\n",
       "      <td>23.618089</td>\n",
       "      <td>0.241209</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>806.406428</td>\n",
       "      <td>28.397296</td>\n",
       "      <td>0.296552</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>803.445529</td>\n",
       "      <td>28.345115</td>\n",
       "      <td>0.295389</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Linear</td>\n",
       "      <td>806.122211</td>\n",
       "      <td>28.392291</td>\n",
       "      <td>0.295594</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Model         MSE       RMSE      MAPE  Rank MSE  Rank RMSE  \\\n",
       "0           Arima    0.480847   0.693431  0.006893       1.0        1.0   \n",
       "1             RNN  803.471095  28.345566  0.292678      11.0       11.0   \n",
       "2         RNN_OPT  768.881366   27.72871  0.283834       7.0        7.0   \n",
       "3       RNN_OPT_D  801.385703  28.308757  0.287185       9.0        9.0   \n",
       "4    RNN_OPT_D_ES  282.412011  16.805119  0.160293       2.0        2.0   \n",
       "5             GRU  792.926785  28.158956  0.288617       8.0        8.0   \n",
       "6         GRU_OPT  854.533657  29.232408  0.307432      15.0       15.0   \n",
       "7       GRU_OPT_D  749.618105  27.379155  0.284273       6.0        6.0   \n",
       "8    GRU_OPT_D_ES  472.601374  21.739397  0.220161       3.0        3.0   \n",
       "9            LSTM  841.367953  29.006343   0.29836      14.0       14.0   \n",
       "10       LSTM_OPT  923.344036  30.386577  0.323354      16.0       16.0   \n",
       "11     LSTM_OPT_D  575.405737  23.987616  0.247318       5.0        5.0   \n",
       "12  LSTM_OPT_D_ES  557.814133  23.618089  0.241209       4.0        4.0   \n",
       "13          Lasso  806.406428  28.397296  0.296552      13.0       13.0   \n",
       "14          Ridge  803.445529  28.345115  0.295389      10.0       10.0   \n",
       "15         Linear  806.122211  28.392291  0.295594      12.0       12.0   \n",
       "\n",
       "    Rank MAPE  \n",
       "0         1.0  \n",
       "1        10.0  \n",
       "2         6.0  \n",
       "3         8.0  \n",
       "4         2.0  \n",
       "5         9.0  \n",
       "6        15.0  \n",
       "7         7.0  \n",
       "8         3.0  \n",
       "9        14.0  \n",
       "10       16.0  \n",
       "11        5.0  \n",
       "12        4.0  \n",
       "13       13.0  \n",
       "14       11.0  \n",
       "15       12.0  "
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ModelComparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15, 10))\n",
    "\n",
    "df_sorted = ModelComparison.sort_values('MSE')\n",
    "# creating the bar plot\n",
    "plt.bar(ModelComparison['Model'], df_sorted['MSE'], width = 0.4)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Model\")\n",
    "plt.ylabel(\"MSE performance\")\n",
    "plt.title(\"MSE Against Model\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15, 10))\n",
    "\n",
    "df_sorted = ModelComparison.sort_values('RMSE')\n",
    "# creating the bar plot\n",
    "plt.bar(ModelComparison['Model'], df_sorted['RMSE'], width = 0.4)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Model\")\n",
    "plt.ylabel(\"RMSE performance\")\n",
    "plt.title(\"RMSE Against Model\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15, 10))\n",
    "\n",
    "df_sorted = ModelComparison.sort_values('MAPE')\n",
    "# creating the bar plot\n",
    "plt.bar(ModelComparison['Model'], df_sorted['MAPE'], width = 0.4)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"Model\")\n",
    "plt.ylabel(\"MAPE performance\")\n",
    "plt.title(\"MAPE Against Model\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### White Noise "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Comparison\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arr_to_df(df1, rownum = 634, start_date = '2017-04-07', end_date = '2018-12-31'):\n",
    "    \"\"\"Convert output of DL models to pd.DataFrame for easy plotting.\"\"\"\n",
    "    #Changing the provided numpy array to pd.DataFrame\n",
    "    df1 = pd.DataFrame(np.reshape(df1, (rownum)))\n",
    "    #Subsetting the dates from reference dataframe\n",
    "    dates = pd.DataFrame(df[start_date: end_date].index)\n",
    "    #Concatenating the dates with the provided dataframe\n",
    "    df1 =pd.concat([df1, dates], axis = 1)\n",
    "    #Setting the dates as index\n",
    "    df1.set_index('Date', drop = True, inplace= True)\n",
    "    return(df1)\n",
    "\n",
    "RNN_Pred = arr_to_df(pred_RNN)\n",
    "RNN_OPT_Pred = arr_to_df(pred_RNN_Opt)\n",
    "RNN_OPT_D_Pred = arr_to_df(pred_RNN_Opt_D)\n",
    "RNN_OPT_D_ES_Pred = arr_to_df(pred_RNN_Opt_D_ES)\n",
    "\n",
    "LSTM_Pred = arr_to_df(pred_LSTM)\n",
    "LSTM_OPT_Pred = arr_to_df(pred_LSTM_Opt)\n",
    "LSTM_OPT_D_Pred = arr_to_df(pred_LSTM_Opt_D)\n",
    "LSTM_OPT_D_ES_Pred = arr_to_df(pred_LSTM_Opt_D_ES)\n",
    "\n",
    "GRU_Pred = arr_to_df(pred_GRU)\n",
    "GRU_OPT_Pred = arr_to_df(pred_GRU_Opt)\n",
    "GRU_OPT_D_Pred = arr_to_df(pred_GRU_Opt_D)\n",
    "GRU_OPT_D_ES_Pred = arr_to_df(pred_GRU_Opt_D_ES)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[185, 142, 23]"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(np.random.choice(range(256), size=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#A54DCA\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "r = lambda: random.randint(0,255)\n",
    "print('#%02X%02X%02X' % (r(),r(),r()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GenerateColour():\n",
    "    import random\n",
    "    r = lambda: random.randint(0,255)\n",
    "    colour ='#%02X%02X%02X' % (r(),r(),r())    \n",
    "\n",
    "\n",
    "    return (colour)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from turtle import color\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(30, 15))\n",
    "ax.plot(tes, label='Real', linewidth=4, color='red')\n",
    "\n",
    "ax.plot(RNN_Pred, label='RNN_Base', linewidth=2,color=GenerateColour())\n",
    "ax.plot(RNN_OPT_D_Pred, label='OPT_D_RNN_Base', linewidth=2,color=GenerateColour())\n",
    "ax.plot(RNN_OPT_Pred, label='OPT_RNN_Base', linewidth=2,color=GenerateColour())\n",
    "ax.plot(RNN_OPT_D_ES_Pred, label='OPT_D_ES_RNN_Base', linewidth=2,color=GenerateColour())\n",
    "\n",
    "ax.plot(LSTM_Pred, label='LSTM_Base', linewidth=2,color=GenerateColour())\n",
    "ax.plot(LSTM_OPT_D_Pred, label='OPT_D_LSTM', linewidth=2,color=GenerateColour())\n",
    "ax.plot(LSTM_OPT_Pred, label='OPT_LSTM', linewidth=2,color=GenerateColour())\n",
    "ax.plot(LSTM_OPT_D_ES_Pred, label='OPT_D_ES_LSTM', linewidth=2,color=GenerateColour())\n",
    "\n",
    "ax.plot(GRU_Pred, label='GRU_Base', linewidth=2,color=GenerateColour())\n",
    "ax.plot(GRU_OPT_D_Pred, label='OPT_D_GRU', linewidth=2,color=GenerateColour())\n",
    "ax.plot(GRU_OPT_Pred, label='OPT_GRU', linewidth=2,color=GenerateColour())\n",
    "ax.plot(GRU_OPT_D_ES_Pred, label='OPT_D_ES_GRU', linewidth=2,color=GenerateColour())\n",
    "\n",
    "ax.plot(ArimaxPred, label='Arima', linewidth=2, color=GenerateColour())\n",
    "\n",
    "ax.plot(pred_Ridge, label='Ridge', linewidth=2,color=GenerateColour())\n",
    "ax.plot(pred_Linear, label='Linear', linewidth=2,color=GenerateColour())\n",
    "ax.plot(pred_Lasso, label='Lasso', linewidth=2,color=GenerateColour())\n",
    "\n",
    "ax.set_ylabel('Price')\n",
    "ax.set_title('Algorithm Prediction on Test set', fontsize=16)\n",
    "ax.legend(loc='best', fontsize=16)\n",
    "plt.show()\n",
    "\n",
    "#change real to different colourt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict the next 7 day price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "SevenDaysToPredict = df_subset.tail(13)\n",
    "\n",
    "te_start = SevenDaysToPredict.index.min()\n",
    "te_end = SevenDaysToPredict.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-12-19 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(te_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "    \n",
    "d = te_end + timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [2019-01-01 00:00:00, 2019-01-02 00:00:00, 2019-01-03 00:00:00, 2019-01-04 00:00:00, 2019-01-05 00:00:00, 2019-01-06 00:00:00, 2019-01-07 00:00:00]"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dti = pd.date_range(d, periods=7, freq=\"D\")\n",
    "dd = pd.DataFrame(pd.date_range(d, periods=7, freq=\"D\"))\n",
    "dd.rename(columns = {dd.columns[0]:'Date'}, inplace = True)\n",
    "dd.set_index(keys='Date', drop= True, inplace= True)\n",
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arima Forecast 7 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>happiness_Log</th>\n",
       "      <th>bittersweet_Log</th>\n",
       "      <th>disgust_Log</th>\n",
       "      <th>disagreement_Log</th>\n",
       "      <th>anxiety_Log</th>\n",
       "      <th>gloominess_Log</th>\n",
       "      <th>distress_Log</th>\n",
       "      <th>violent_Log</th>\n",
       "      <th>unexpectedness_Log</th>\n",
       "      <th>...</th>\n",
       "      <th>month-12</th>\n",
       "      <th>dayofweek-0</th>\n",
       "      <th>dayofweek-1</th>\n",
       "      <th>dayofweek-2</th>\n",
       "      <th>dayofweek-3</th>\n",
       "      <th>dayofweek-4</th>\n",
       "      <th>dayofweek-5</th>\n",
       "      <th>dayofweek-6</th>\n",
       "      <th>weekend</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-25</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>0.026746</td>\n",
       "      <td>0.007515</td>\n",
       "      <td>0.054204</td>\n",
       "      <td>0.064133</td>\n",
       "      <td>0.075361</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-26</th>\n",
       "      <td>53.425344</td>\n",
       "      <td>0.029797</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>0.003995</td>\n",
       "      <td>-0.004322</td>\n",
       "      <td>0.007022</td>\n",
       "      <td>0.021375</td>\n",
       "      <td>0.022784</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>55.956283</td>\n",
       "      <td>0.004898</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.013357</td>\n",
       "      <td>0.008773</td>\n",
       "      <td>0.015040</td>\n",
       "      <td>0.023654</td>\n",
       "      <td>0.026271</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.008539</td>\n",
       "      <td>0.001984</td>\n",
       "      <td>-0.000320</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>0.030511</td>\n",
       "      <td>0.033064</td>\n",
       "      <td>0.023133</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-29</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.005656</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>-0.004642</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>0.039399</td>\n",
       "      <td>0.042366</td>\n",
       "      <td>0.009745</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>0.002456</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>-0.011601</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.016210</td>\n",
       "      <td>0.008824</td>\n",
       "      <td>0.012188</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.009354</td>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.003139</td>\n",
       "      <td>-0.000502</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.052269</td>\n",
       "      <td>0.032805</td>\n",
       "      <td>0.019615</td>\n",
       "      <td>0.004176</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                price  happiness_Log  bittersweet_Log  disgust_Log  \\\n",
       "Date                                                                 \n",
       "2018-12-25  56.906192       0.001523         0.001523     0.003886   \n",
       "2018-12-26  53.425344       0.029797         0.010036     0.003995   \n",
       "2018-12-27  55.956283       0.004898         0.000031     0.002467   \n",
       "2018-12-28  56.043514       0.008539         0.001984    -0.000320   \n",
       "2018-12-29  56.043514       0.005656         0.003743     0.002063   \n",
       "2018-12-30  56.043514       0.005449         0.002456     0.008150   \n",
       "2018-12-31  56.043514       0.009354         0.003401     0.003139   \n",
       "\n",
       "            disagreement_Log  anxiety_Log  gloominess_Log  distress_Log  \\\n",
       "Date                                                                      \n",
       "2018-12-25          0.026746     0.007515        0.054204      0.064133   \n",
       "2018-12-26         -0.004322     0.007022        0.021375      0.022784   \n",
       "2018-12-27          0.013357     0.008773        0.015040      0.023654   \n",
       "2018-12-28          0.000009     0.004611        0.030511      0.033064   \n",
       "2018-12-29         -0.004642     0.002063        0.039399      0.042366   \n",
       "2018-12-30         -0.011601     0.000024        0.016210      0.008824   \n",
       "2018-12-31         -0.000502     0.007800        0.052269      0.032805   \n",
       "\n",
       "            violent_Log  unexpectedness_Log  ...  month-12  dayofweek-0  \\\n",
       "Date                                         ...                          \n",
       "2018-12-25     0.075361            0.001523  ...         1            0   \n",
       "2018-12-26     0.010036            0.001237  ...         1            0   \n",
       "2018-12-27     0.026271            0.000519  ...         1            0   \n",
       "2018-12-28     0.023133            0.001326  ...         1            0   \n",
       "2018-12-29     0.009745            0.000005  ...         1            0   \n",
       "2018-12-30     0.012188            0.001383  ...         1            0   \n",
       "2018-12-31     0.019615            0.004176  ...         1            1   \n",
       "\n",
       "            dayofweek-1  dayofweek-2  dayofweek-3  dayofweek-4  dayofweek-5  \\\n",
       "Date                                                                          \n",
       "2018-12-25            1            0            0            0            0   \n",
       "2018-12-26            0            1            0            0            0   \n",
       "2018-12-27            0            0            1            0            0   \n",
       "2018-12-28            0            0            0            1            0   \n",
       "2018-12-29            0            0            0            0            1   \n",
       "2018-12-30            0            0            0            0            0   \n",
       "2018-12-31            0            0            0            0            0   \n",
       "\n",
       "            dayofweek-6  weekend  sunday  \n",
       "Date                                      \n",
       "2018-12-25            0        0       0  \n",
       "2018-12-26            0        0       0  \n",
       "2018-12-27            0        0       0  \n",
       "2018-12-28            0        0       0  \n",
       "2018-12-29            0        1       0  \n",
       "2018-12-30            1        1       1  \n",
       "2018-12-31            0        0       0  \n",
       "\n",
       "[7 rows x 53 columns]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#arima only need 7 days before to predict.\n",
    "SevenDaysToPredictArima = df_subset.tail(7)\n",
    "\n",
    "SevenDaysToPredictArima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2019-01-01</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>0.029797</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>0.003995</td>\n",
       "      <td>-0.004322</td>\n",
       "      <td>0.007022</td>\n",
       "      <td>0.021375</td>\n",
       "      <td>0.022784</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>0.022784</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>0.004898</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.013357</td>\n",
       "      <td>0.008773</td>\n",
       "      <td>0.015040</td>\n",
       "      <td>0.023654</td>\n",
       "      <td>0.026271</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>0.007322</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>0.008539</td>\n",
       "      <td>0.001984</td>\n",
       "      <td>-0.000320</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>0.030511</td>\n",
       "      <td>0.033064</td>\n",
       "      <td>0.023133</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>0.009845</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>0.005656</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>-0.004642</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>0.039399</td>\n",
       "      <td>0.042366</td>\n",
       "      <td>0.009745</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.003604</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>0.005449</td>\n",
       "      <td>0.002456</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>-0.011601</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.016210</td>\n",
       "      <td>0.008824</td>\n",
       "      <td>0.012188</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>0.004095</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>0.009354</td>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.003139</td>\n",
       "      <td>-0.000502</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.052269</td>\n",
       "      <td>0.032805</td>\n",
       "      <td>0.019615</td>\n",
       "      <td>0.004176</td>\n",
       "      <td>0.005213</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            happiness_Log  bittersweet_Log  disgust_Log  disagreement_Log  \\\n",
       "2019-01-01       0.001523         0.001523     0.003886          0.026746   \n",
       "2019-01-02       0.029797         0.010036     0.003995         -0.004322   \n",
       "2019-01-03       0.004898         0.000031     0.002467          0.013357   \n",
       "2019-01-04       0.008539         0.001984    -0.000320          0.000009   \n",
       "2019-01-05       0.005656         0.003743     0.002063         -0.004642   \n",
       "2019-01-06       0.005449         0.002456     0.008150         -0.011601   \n",
       "2019-01-07       0.009354         0.003401     0.003139         -0.000502   \n",
       "\n",
       "            anxiety_Log  gloominess_Log  distress_Log  violent_Log  \\\n",
       "2019-01-01     0.007515        0.054204      0.064133     0.075361   \n",
       "2019-01-02     0.007022        0.021375      0.022784     0.010036   \n",
       "2019-01-03     0.008773        0.015040      0.023654     0.026271   \n",
       "2019-01-04     0.004611        0.030511      0.033064     0.023133   \n",
       "2019-01-05     0.002063        0.039399      0.042366     0.009745   \n",
       "2019-01-06     0.000024        0.016210      0.008824     0.012188   \n",
       "2019-01-07     0.007800        0.052269      0.032805     0.019615   \n",
       "\n",
       "            unexpectedness_Log  confusion_Log  ...  month-12  dayofweek-0  \\\n",
       "2019-01-01            0.001523       0.004524  ...         1            0   \n",
       "2019-01-02            0.001237       0.022784  ...         1            0   \n",
       "2019-01-03            0.000519       0.007322  ...         1            0   \n",
       "2019-01-04            0.001326       0.009845  ...         1            0   \n",
       "2019-01-05            0.000005       0.003604  ...         1            0   \n",
       "2019-01-06            0.001383       0.004095  ...         1            0   \n",
       "2019-01-07            0.004176       0.005213  ...         1            1   \n",
       "\n",
       "            dayofweek-1  dayofweek-2  dayofweek-3  dayofweek-4  dayofweek-5  \\\n",
       "2019-01-01            1            0            0            0            0   \n",
       "2019-01-02            0            1            0            0            0   \n",
       "2019-01-03            0            0            1            0            0   \n",
       "2019-01-04            0            0            0            1            0   \n",
       "2019-01-05            0            0            0            0            1   \n",
       "2019-01-06            0            0            0            0            0   \n",
       "2019-01-07            0            0            0            0            0   \n",
       "\n",
       "            dayofweek-6  weekend  sunday  \n",
       "2019-01-01            0        0       0  \n",
       "2019-01-02            0        0       0  \n",
       "2019-01-03            0        0       0  \n",
       "2019-01-04            0        0       0  \n",
       "2019-01-05            0        1       0  \n",
       "2019-01-06            1        1       1  \n",
       "2019-01-07            0        0       0  \n",
       "\n",
       "[7 rows x 52 columns]"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exog_features_first640 = exog_test.copy()\n",
    "exog_features_forecasted = SevenDaysToPredictArima.drop(axis=1, columns='price').columns\n",
    "exog_features_forecasted = SevenDaysToPredictArima[te_start : te_end].loc[:, exog_features_forecasted]\n",
    "exog_features_forecasted.index = ['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04', '2019-01-05', '2019-01-06', '2019-01-07']\n",
    "exog_features_forecasted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-04-01    56.760617\n",
      "2017-04-02    56.214125\n",
      "2017-04-03    56.133761\n",
      "2017-04-04    56.373590\n",
      "2017-04-05    55.959304\n",
      "2017-04-06    55.925832\n",
      "2017-04-07    55.856457\n",
      "Freq: D, Name: predicted_mean, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "exog_features = SevenDaysToPredictArima.drop(axis=1, columns='price').columns\n",
    "exog_test = SevenDaysToPredictArima[te_start : te_end].loc[:, exog_features]\n",
    "tes = SevenDaysToPredictArima[te_start : te_end].loc[:, 'price']\n",
    "\n",
    "forecast= arimax.forecast(steps=7,exog = exog_test)\n",
    "\n",
    "print(forecast)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "647\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\2773018069.py:3: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  exog_features_final = exog_features_first640.append(exog_features_forecasted)\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\2773018069.py:11: FutureWarning: Inferring datetime64[ns] from data containing strings is deprecated and will be removed in a future version. To retain the old behavior explicitly pass Series(data, dtype=datetime64[ns])\n",
      "  st1 = str(exog_features_final.reset_index().iloc[0,0])[:10]\n",
      "C:\\Users\\sidizainulabideen.a\\AppData\\Local\\Temp\\ipykernel_10176\\2773018069.py:12: FutureWarning: Inferring datetime64[ns] from data containing strings is deprecated and will be removed in a future version. To retain the old behavior explicitly pass Series(data, dtype=datetime64[ns])\n",
      "  en1 = str(exog_features_final.reset_index().iloc[-1,0])[:10]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2019-01-01    56.647723\n",
       "2019-01-02    56.176984\n",
       "2019-01-03    56.121542\n",
       "2019-01-04    56.369570\n",
       "2019-01-05    55.957982\n",
       "2019-01-06    55.925397\n",
       "2019-01-07    55.856314\n",
       "Freq: D, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 604,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "tes = SevenDaysToPredictArima[te_start : te_end].loc[:, 'price']\n",
    "exog_features_final = exog_features_first640.append(exog_features_forecasted)\n",
    "print(len(exog_features_final))\n",
    "\n",
    "# st = str(exog_features_forecasted.reset_index().iloc[0,0])[:10]\n",
    "# en = str(exog_features_forecasted.reset_index().iloc[-1,0])[:10]\n",
    "# print(st, en)\n",
    "# forecast= arimax.predict(st, en, exog = exog_features_forecasted)\n",
    "\n",
    "st1 = str(exog_features_final.reset_index().iloc[0,0])[:10]\n",
    "en1 = str(exog_features_final.reset_index().iloc[-1,0])[:10]\n",
    "predBest = arimax.predict(st1, en1, exog = exog_features_final)[-7:]\n",
    "\n",
    "predBest\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-04-01    56.760617\n",
       "2017-04-02    56.214125\n",
       "2017-04-03    56.133761\n",
       "2017-04-04    56.373590\n",
       "2017-04-05    55.959304\n",
       "2017-04-06    55.925832\n",
       "2017-04-07    55.856457\n",
       "Freq: D, Name: predicted_mean, dtype: float64"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arima_dataframe(dd,forecast_series):\n",
    "    arimaforecast = dd\n",
    "    arimaforecast[\"Arima\"] = forecast_series.values\n",
    "    \n",
    "    return (arimaforecast)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "arimaforecast = arima_dataframe (dd,forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([56.76061728, 56.21412472, 56.13376134, 56.37359001, 55.95930439,\n",
       "       55.92583214, 55.85645715])"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RNN forecast 7 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "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>price</th>\n",
       "      <th>happiness_Log</th>\n",
       "      <th>bittersweet_Log</th>\n",
       "      <th>disgust_Log</th>\n",
       "      <th>disagreement_Log</th>\n",
       "      <th>anxiety_Log</th>\n",
       "      <th>gloominess_Log</th>\n",
       "      <th>distress_Log</th>\n",
       "      <th>violent_Log</th>\n",
       "      <th>unexpectedness_Log</th>\n",
       "      <th>...</th>\n",
       "      <th>month-12</th>\n",
       "      <th>dayofweek-0</th>\n",
       "      <th>dayofweek-1</th>\n",
       "      <th>dayofweek-2</th>\n",
       "      <th>dayofweek-3</th>\n",
       "      <th>dayofweek-4</th>\n",
       "      <th>dayofweek-5</th>\n",
       "      <th>dayofweek-6</th>\n",
       "      <th>weekend</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-19</th>\n",
       "      <td>58.459646</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>0.001916</td>\n",
       "      <td>0.003342</td>\n",
       "      <td>-0.003569</td>\n",
       "      <td>0.005951</td>\n",
       "      <td>0.011148</td>\n",
       "      <td>0.024723</td>\n",
       "      <td>0.019595</td>\n",
       "      <td>-0.000942</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-20</th>\n",
       "      <td>57.753073</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.005391</td>\n",
       "      <td>0.023895</td>\n",
       "      <td>0.004758</td>\n",
       "      <td>0.047236</td>\n",
       "      <td>0.074190</td>\n",
       "      <td>0.010128</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-21</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.002501</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.002088</td>\n",
       "      <td>0.025999</td>\n",
       "      <td>0.003741</td>\n",
       "      <td>0.057605</td>\n",
       "      <td>0.105550</td>\n",
       "      <td>0.044210</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-22</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.008448</td>\n",
       "      <td>0.000846</td>\n",
       "      <td>0.003393</td>\n",
       "      <td>0.015147</td>\n",
       "      <td>0.003393</td>\n",
       "      <td>0.020143</td>\n",
       "      <td>0.019313</td>\n",
       "      <td>0.088360</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-23</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.002569</td>\n",
       "      <td>0.010154</td>\n",
       "      <td>0.005104</td>\n",
       "      <td>0.039932</td>\n",
       "      <td>0.011412</td>\n",
       "      <td>0.018931</td>\n",
       "      <td>0.042374</td>\n",
       "      <td>0.049665</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-24</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.012266</td>\n",
       "      <td>-0.001332</td>\n",
       "      <td>0.003997</td>\n",
       "      <td>0.001669</td>\n",
       "      <td>0.011277</td>\n",
       "      <td>0.032483</td>\n",
       "      <td>0.040841</td>\n",
       "      <td>0.006982</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-25</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>0.026746</td>\n",
       "      <td>0.007515</td>\n",
       "      <td>0.054204</td>\n",
       "      <td>0.064133</td>\n",
       "      <td>0.075361</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-26</th>\n",
       "      <td>53.425344</td>\n",
       "      <td>0.029797</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>0.003995</td>\n",
       "      <td>-0.004322</td>\n",
       "      <td>0.007022</td>\n",
       "      <td>0.021375</td>\n",
       "      <td>0.022784</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>55.956283</td>\n",
       "      <td>0.004898</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.013357</td>\n",
       "      <td>0.008773</td>\n",
       "      <td>0.015040</td>\n",
       "      <td>0.023654</td>\n",
       "      <td>0.026271</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.008539</td>\n",
       "      <td>0.001984</td>\n",
       "      <td>-0.000320</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>0.030511</td>\n",
       "      <td>0.033064</td>\n",
       "      <td>0.023133</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-29</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.005656</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>-0.004642</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>0.039399</td>\n",
       "      <td>0.042366</td>\n",
       "      <td>0.009745</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>0.002456</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>-0.011601</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.016210</td>\n",
       "      <td>0.008824</td>\n",
       "      <td>0.012188</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.009354</td>\n",
       "      <td>0.003401</td>\n",
       "      <td>0.003139</td>\n",
       "      <td>-0.000502</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.052269</td>\n",
       "      <td>0.032805</td>\n",
       "      <td>0.019615</td>\n",
       "      <td>0.004176</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                price  happiness_Log  bittersweet_Log  disgust_Log  \\\n",
       "Date                                                                 \n",
       "2018-12-19  58.459646       0.001678         0.001916     0.003342   \n",
       "2018-12-20  57.753073       0.002913         0.000631     0.005391   \n",
       "2018-12-21  56.906192       0.002501         0.001673     0.002088   \n",
       "2018-12-22  56.906192       0.008448         0.000846     0.003393   \n",
       "2018-12-23  56.906192       0.002569         0.010154     0.005104   \n",
       "2018-12-24  56.906192       0.012266        -0.001332     0.003997   \n",
       "2018-12-25  56.906192       0.001523         0.001523     0.003886   \n",
       "2018-12-26  53.425344       0.029797         0.010036     0.003995   \n",
       "2018-12-27  55.956283       0.004898         0.000031     0.002467   \n",
       "2018-12-28  56.043514       0.008539         0.001984    -0.000320   \n",
       "2018-12-29  56.043514       0.005656         0.003743     0.002063   \n",
       "2018-12-30  56.043514       0.005449         0.002456     0.008150   \n",
       "2018-12-31  56.043514       0.009354         0.003401     0.003139   \n",
       "\n",
       "            disagreement_Log  anxiety_Log  gloominess_Log  distress_Log  \\\n",
       "Date                                                                      \n",
       "2018-12-19         -0.003569     0.005951        0.011148      0.024723   \n",
       "2018-12-20          0.023895     0.004758        0.047236      0.074190   \n",
       "2018-12-21          0.025999     0.003741        0.057605      0.105550   \n",
       "2018-12-22          0.015147     0.003393        0.020143      0.019313   \n",
       "2018-12-23          0.039932     0.011412        0.018931      0.042374   \n",
       "2018-12-24          0.001669     0.011277        0.032483      0.040841   \n",
       "2018-12-25          0.026746     0.007515        0.054204      0.064133   \n",
       "2018-12-26         -0.004322     0.007022        0.021375      0.022784   \n",
       "2018-12-27          0.013357     0.008773        0.015040      0.023654   \n",
       "2018-12-28          0.000009     0.004611        0.030511      0.033064   \n",
       "2018-12-29         -0.004642     0.002063        0.039399      0.042366   \n",
       "2018-12-30         -0.011601     0.000024        0.016210      0.008824   \n",
       "2018-12-31         -0.000502     0.007800        0.052269      0.032805   \n",
       "\n",
       "            violent_Log  unexpectedness_Log  ...  month-12  dayofweek-0  \\\n",
       "Date                                         ...                          \n",
       "2018-12-19     0.019595           -0.000942  ...         1            0   \n",
       "2018-12-20     0.010128           -0.000005  ...         1            0   \n",
       "2018-12-21     0.044210            0.002294  ...         1            0   \n",
       "2018-12-22     0.088360            0.001702  ...         1            0   \n",
       "2018-12-23     0.049665            0.001267  ...         1            0   \n",
       "2018-12-24     0.006982            0.000670  ...         1            1   \n",
       "2018-12-25     0.075361            0.001523  ...         1            0   \n",
       "2018-12-26     0.010036            0.001237  ...         1            0   \n",
       "2018-12-27     0.026271            0.000519  ...         1            0   \n",
       "2018-12-28     0.023133            0.001326  ...         1            0   \n",
       "2018-12-29     0.009745            0.000005  ...         1            0   \n",
       "2018-12-30     0.012188            0.001383  ...         1            0   \n",
       "2018-12-31     0.019615            0.004176  ...         1            1   \n",
       "\n",
       "            dayofweek-1  dayofweek-2  dayofweek-3  dayofweek-4  dayofweek-5  \\\n",
       "Date                                                                          \n",
       "2018-12-19            0            1            0            0            0   \n",
       "2018-12-20            0            0            1            0            0   \n",
       "2018-12-21            0            0            0            1            0   \n",
       "2018-12-22            0            0            0            0            1   \n",
       "2018-12-23            0            0            0            0            0   \n",
       "2018-12-24            0            0            0            0            0   \n",
       "2018-12-25            1            0            0            0            0   \n",
       "2018-12-26            0            1            0            0            0   \n",
       "2018-12-27            0            0            1            0            0   \n",
       "2018-12-28            0            0            0            1            0   \n",
       "2018-12-29            0            0            0            0            1   \n",
       "2018-12-30            0            0            0            0            0   \n",
       "2018-12-31            0            0            0            0            0   \n",
       "\n",
       "            dayofweek-6  weekend  sunday  \n",
       "Date                                      \n",
       "2018-12-19            0        0       0  \n",
       "2018-12-20            0        0       0  \n",
       "2018-12-21            0        0       0  \n",
       "2018-12-22            0        1       0  \n",
       "2018-12-23            1        1       1  \n",
       "2018-12-24            0        0       0  \n",
       "2018-12-25            0        0       0  \n",
       "2018-12-26            0        0       0  \n",
       "2018-12-27            0        0       0  \n",
       "2018-12-28            0        0       0  \n",
       "2018-12-29            0        1       0  \n",
       "2018-12-30            1        1       1  \n",
       "2018-12-31            0        0       0  \n",
       "\n",
       "[13 rows x 53 columns]"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SevenDaysToPredict = df_subset.tail(13)\n",
    "\n",
    "SevenDaysToPredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Standardization(dataset,x_scaler):\n",
    "\n",
    "    x_scaled = x_scaler.fit_transform(dataset.iloc[:, 1:])\n",
    "    y = np.reshape(np.array(dataset.iloc[:,0]), (-1,1))\n",
    "    data_scaled = np.hstack((x_scaled, y))\n",
    "\n",
    "    return (data_scaled)\n",
    "\n",
    "data_scaled = Standardization(SevenDaysToPredict,x_scaler)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predictors_split_sequences(sequences, n_steps):\n",
    "\tX, y = list(), list()\n",
    "\tfor i in range(len(sequences)):\n",
    "\t\t# find the end of this pattern\n",
    "\t\tend_ix = i + n_steps\n",
    "\t\t# check if we are beyond the dataset\n",
    "\t\tif end_ix > len(sequences):\n",
    "\t\t\tbreak\n",
    "\t\t# gather input and output parts of the pattern\n",
    "\t\tseq_x, seq_y = sequences[i:end_ix, :-1], sequences[end_ix-1, -1]\n",
    "\t\tX.append(seq_x)\n",
    "\t\ty.append(seq_y)\n",
    "\treturn np.array(X), np.array(y)\n",
    "\n",
    "x_testDL, y_testDL = predictors_split_sequences(data_scaled, 7)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arr_to_df_f(df1,col_name, rownum = 7, start_date = '2019-01-01', end_date = '2019-01-07'):\n",
    "    \"\"\"Convert output of DL models to pd.DataFrame for easy plotting.\"\"\"\n",
    "    #Changing the provided numpy array to pd.DataFrame\n",
    "    df1 = pd.DataFrame(np.reshape(df1, (rownum)))\n",
    "    #Subsetting the dates from reference dataframe\n",
    "    dates = pd.DataFrame(dd[start_date: end_date].index)\n",
    "    #Concatenating the dates with the provided dataframe\n",
    "    df1 =pd.concat([df1, dates], axis = 1)\n",
    "    #Setting the dates as index\n",
    "    df1.set_index('Date', drop = True, inplace= True)\n",
    "    df1.rename(columns = {0: col_name}, inplace= True)\n",
    "    return(df1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 63ms/step\n",
      "1/1 [==============================] - 1s 592ms/step\n",
      "1/1 [==============================] - 1s 720ms/step\n",
      "1/1 [==============================] - 1s 750ms/step\n"
     ]
    }
   ],
   "source": [
    "pred_RNN = RNN_base.predict(x_testDL)\n",
    "pred_RNN_OPT_D_ES = RNN_Opt_D_ES.predict(x_testDL)\n",
    "pred_RNN_OPT_D = RNN_Opt_D.predict(x_testDL)\n",
    "pred_RNN_OPT = RNN_Opt.predict(x_testDL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "RNN_Pred = arr_to_df_f(pred_RNN,'RNN')\n",
    "pred_RNN_OPT_D_ES = arr_to_df_f(pred_RNN_OPT_D_ES)\n",
    "pred_RNN_OPT_D = arr_to_df_f(pred_RNN_OPT_D)\n",
    "pred_RNN_OPT = arr_to_df_f(pred_RNN_OPT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RNN_Optimised</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>118.393799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>106.134354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>92.279877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>82.044579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>74.169136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>71.026085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>63.737103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RNN_Optimised\n",
       "Date                     \n",
       "2019-01-01     118.393799\n",
       "2019-01-02     106.134354\n",
       "2019-01-03      92.279877\n",
       "2019-01-04      82.044579\n",
       "2019-01-05      74.169136\n",
       "2019-01-06      71.026085\n",
       "2019-01-07      63.737103"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_RNN_OPT.rename(columns = {0: 'RNN_Optimised'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ML "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>happiness_Log</th>\n",
       "      <th>bittersweet_Log</th>\n",
       "      <th>disgust_Log</th>\n",
       "      <th>disagreement_Log</th>\n",
       "      <th>anxiety_Log</th>\n",
       "      <th>gloominess_Log</th>\n",
       "      <th>distress_Log</th>\n",
       "      <th>violent_Log</th>\n",
       "      <th>unexpectedness_Log</th>\n",
       "      <th>...</th>\n",
       "      <th>month-12</th>\n",
       "      <th>dayofweek-0</th>\n",
       "      <th>dayofweek-1</th>\n",
       "      <th>dayofweek-2</th>\n",
       "      <th>dayofweek-3</th>\n",
       "      <th>dayofweek-4</th>\n",
       "      <th>dayofweek-5</th>\n",
       "      <th>dayofweek-6</th>\n",
       "      <th>weekend</th>\n",
       "      <th>sunday</th>\n",
       "    </tr>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-18</th>\n",
       "      <td>60.651416</td>\n",
       "      <td>-0.000869</td>\n",
       "      <td>3.378769e-07</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>0.017648</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>0.040843</td>\n",
       "      <td>0.049560</td>\n",
       "      <td>0.054095</td>\n",
       "      <td>0.002603</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-19</th>\n",
       "      <td>58.459646</td>\n",
       "      <td>0.001678</td>\n",
       "      <td>1.915849e-03</td>\n",
       "      <td>0.003342</td>\n",
       "      <td>-0.003569</td>\n",
       "      <td>0.005951</td>\n",
       "      <td>0.011148</td>\n",
       "      <td>0.024723</td>\n",
       "      <td>0.019595</td>\n",
       "      <td>-0.000942</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-20</th>\n",
       "      <td>57.753073</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>6.311580e-04</td>\n",
       "      <td>0.005391</td>\n",
       "      <td>0.023895</td>\n",
       "      <td>0.004758</td>\n",
       "      <td>0.047236</td>\n",
       "      <td>0.074190</td>\n",
       "      <td>0.010128</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-21</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.002501</td>\n",
       "      <td>1.673422e-03</td>\n",
       "      <td>0.002088</td>\n",
       "      <td>0.025999</td>\n",
       "      <td>0.003741</td>\n",
       "      <td>0.057605</td>\n",
       "      <td>0.105550</td>\n",
       "      <td>0.044210</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-22</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.008448</td>\n",
       "      <td>8.458147e-04</td>\n",
       "      <td>0.003393</td>\n",
       "      <td>0.015147</td>\n",
       "      <td>0.003393</td>\n",
       "      <td>0.020143</td>\n",
       "      <td>0.019313</td>\n",
       "      <td>0.088360</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-23</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.002569</td>\n",
       "      <td>1.015385e-02</td>\n",
       "      <td>0.005104</td>\n",
       "      <td>0.039932</td>\n",
       "      <td>0.011412</td>\n",
       "      <td>0.018931</td>\n",
       "      <td>0.042374</td>\n",
       "      <td>0.049665</td>\n",
       "      <td>0.001267</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-24</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.012266</td>\n",
       "      <td>-1.331636e-03</td>\n",
       "      <td>0.003997</td>\n",
       "      <td>0.001669</td>\n",
       "      <td>0.011277</td>\n",
       "      <td>0.032483</td>\n",
       "      <td>0.040841</td>\n",
       "      <td>0.006982</td>\n",
       "      <td>0.000670</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-25</th>\n",
       "      <td>56.906192</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>1.522762e-03</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>0.026746</td>\n",
       "      <td>0.007515</td>\n",
       "      <td>0.054204</td>\n",
       "      <td>0.064133</td>\n",
       "      <td>0.075361</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-26</th>\n",
       "      <td>53.425344</td>\n",
       "      <td>0.029797</td>\n",
       "      <td>1.003595e-02</td>\n",
       "      <td>0.003995</td>\n",
       "      <td>-0.004322</td>\n",
       "      <td>0.007022</td>\n",
       "      <td>0.021375</td>\n",
       "      <td>0.022784</td>\n",
       "      <td>0.010036</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-27</th>\n",
       "      <td>55.956283</td>\n",
       "      <td>0.004898</td>\n",
       "      <td>3.075233e-05</td>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.013357</td>\n",
       "      <td>0.008773</td>\n",
       "      <td>0.015040</td>\n",
       "      <td>0.023654</td>\n",
       "      <td>0.026271</td>\n",
       "      <td>0.000519</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-28</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.008539</td>\n",
       "      <td>1.983758e-03</td>\n",
       "      <td>-0.000320</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.004611</td>\n",
       "      <td>0.030511</td>\n",
       "      <td>0.033064</td>\n",
       "      <td>0.023133</td>\n",
       "      <td>0.001326</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-29</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.005656</td>\n",
       "      <td>3.743270e-03</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>-0.004642</td>\n",
       "      <td>0.002063</td>\n",
       "      <td>0.039399</td>\n",
       "      <td>0.042366</td>\n",
       "      <td>0.009745</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.005449</td>\n",
       "      <td>2.456208e-03</td>\n",
       "      <td>0.008150</td>\n",
       "      <td>-0.011601</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.016210</td>\n",
       "      <td>0.008824</td>\n",
       "      <td>0.012188</td>\n",
       "      <td>0.001383</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-31</th>\n",
       "      <td>56.043514</td>\n",
       "      <td>0.009354</td>\n",
       "      <td>3.400931e-03</td>\n",
       "      <td>0.003139</td>\n",
       "      <td>-0.000502</td>\n",
       "      <td>0.007800</td>\n",
       "      <td>0.052269</td>\n",
       "      <td>0.032805</td>\n",
       "      <td>0.019615</td>\n",
       "      <td>0.004176</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                price  happiness_Log  bittersweet_Log  disgust_Log  \\\n",
       "Date                                                                 \n",
       "2018-12-18  60.651416      -0.000869     3.378769e-07     0.003468   \n",
       "2018-12-19  58.459646       0.001678     1.915849e-03     0.003342   \n",
       "2018-12-20  57.753073       0.002913     6.311580e-04     0.005391   \n",
       "2018-12-21  56.906192       0.002501     1.673422e-03     0.002088   \n",
       "2018-12-22  56.906192       0.008448     8.458147e-04     0.003393   \n",
       "2018-12-23  56.906192       0.002569     1.015385e-02     0.005104   \n",
       "2018-12-24  56.906192       0.012266    -1.331636e-03     0.003997   \n",
       "2018-12-25  56.906192       0.001523     1.522762e-03     0.003886   \n",
       "2018-12-26  53.425344       0.029797     1.003595e-02     0.003995   \n",
       "2018-12-27  55.956283       0.004898     3.075233e-05     0.002467   \n",
       "2018-12-28  56.043514       0.008539     1.983758e-03    -0.000320   \n",
       "2018-12-29  56.043514       0.005656     3.743270e-03     0.002063   \n",
       "2018-12-30  56.043514       0.005449     2.456208e-03     0.008150   \n",
       "2018-12-31  56.043514       0.009354     3.400931e-03     0.003139   \n",
       "\n",
       "            disagreement_Log  anxiety_Log  gloominess_Log  distress_Log  \\\n",
       "Date                                                                      \n",
       "2018-12-18          0.017648     0.003468        0.040843      0.049560   \n",
       "2018-12-19         -0.003569     0.005951        0.011148      0.024723   \n",
       "2018-12-20          0.023895     0.004758        0.047236      0.074190   \n",
       "2018-12-21          0.025999     0.003741        0.057605      0.105550   \n",
       "2018-12-22          0.015147     0.003393        0.020143      0.019313   \n",
       "2018-12-23          0.039932     0.011412        0.018931      0.042374   \n",
       "2018-12-24          0.001669     0.011277        0.032483      0.040841   \n",
       "2018-12-25          0.026746     0.007515        0.054204      0.064133   \n",
       "2018-12-26         -0.004322     0.007022        0.021375      0.022784   \n",
       "2018-12-27          0.013357     0.008773        0.015040      0.023654   \n",
       "2018-12-28          0.000009     0.004611        0.030511      0.033064   \n",
       "2018-12-29         -0.004642     0.002063        0.039399      0.042366   \n",
       "2018-12-30         -0.011601     0.000024        0.016210      0.008824   \n",
       "2018-12-31         -0.000502     0.007800        0.052269      0.032805   \n",
       "\n",
       "            violent_Log  unexpectedness_Log  ...  month-12  dayofweek-0  \\\n",
       "Date                                         ...                          \n",
       "2018-12-18     0.054095            0.002603  ...         1            0   \n",
       "2018-12-19     0.019595           -0.000942  ...         1            0   \n",
       "2018-12-20     0.010128           -0.000005  ...         1            0   \n",
       "2018-12-21     0.044210            0.002294  ...         1            0   \n",
       "2018-12-22     0.088360            0.001702  ...         1            0   \n",
       "2018-12-23     0.049665            0.001267  ...         1            0   \n",
       "2018-12-24     0.006982            0.000670  ...         1            1   \n",
       "2018-12-25     0.075361            0.001523  ...         1            0   \n",
       "2018-12-26     0.010036            0.001237  ...         1            0   \n",
       "2018-12-27     0.026271            0.000519  ...         1            0   \n",
       "2018-12-28     0.023133            0.001326  ...         1            0   \n",
       "2018-12-29     0.009745            0.000005  ...         1            0   \n",
       "2018-12-30     0.012188            0.001383  ...         1            0   \n",
       "2018-12-31     0.019615            0.004176  ...         1            1   \n",
       "\n",
       "            dayofweek-1  dayofweek-2  dayofweek-3  dayofweek-4  dayofweek-5  \\\n",
       "Date                                                                          \n",
       "2018-12-18            1            0            0            0            0   \n",
       "2018-12-19            0            1            0            0            0   \n",
       "2018-12-20            0            0            1            0            0   \n",
       "2018-12-21            0            0            0            1            0   \n",
       "2018-12-22            0            0            0            0            1   \n",
       "2018-12-23            0            0            0            0            0   \n",
       "2018-12-24            0            0            0            0            0   \n",
       "2018-12-25            1            0            0            0            0   \n",
       "2018-12-26            0            1            0            0            0   \n",
       "2018-12-27            0            0            1            0            0   \n",
       "2018-12-28            0            0            0            1            0   \n",
       "2018-12-29            0            0            0            0            1   \n",
       "2018-12-30            0            0            0            0            0   \n",
       "2018-12-31            0            0            0            0            0   \n",
       "\n",
       "            dayofweek-6  weekend  sunday  \n",
       "Date                                      \n",
       "2018-12-18            0        0       0  \n",
       "2018-12-19            0        0       0  \n",
       "2018-12-20            0        0       0  \n",
       "2018-12-21            0        0       0  \n",
       "2018-12-22            0        1       0  \n",
       "2018-12-23            1        1       1  \n",
       "2018-12-24            0        0       0  \n",
       "2018-12-25            0        0       0  \n",
       "2018-12-26            0        0       0  \n",
       "2018-12-27            0        0       0  \n",
       "2018-12-28            0        0       0  \n",
       "2018-12-29            0        1       0  \n",
       "2018-12-30            1        1       1  \n",
       "2018-12-31            0        0       0  \n",
       "\n",
       "[14 rows x 53 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SevenDaysToPredictML = df_subset.tail(14)\n",
    "\n",
    "SevenDaysToPredictML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14, 50)\n"
     ]
    }
   ],
   "source": [
    "X_ML = SevenDaysToPredictML[selected_variable_columns]\n",
    "\n",
    "engineered_features = X_ML[['price_lag_7', 'price_rm', 'price_ewm']]\n",
    "\n",
    "\n",
    "\n",
    "X_ML.drop(['price_lag_7', 'price_rm', 'price_ewm'], inplace= True, axis = 1)\n",
    "\n",
    "print(X_ML.shape)\n",
    "\n",
    "ML_X = create_lagged_features(X_ML, selected_variable_columns_ML, lag_number = 7)\n",
    "\n",
    "ML_X = pd.concat([ML_X, engineered_features], axis = 1)\n",
    "\n",
    "ML_X.drop(['price'], inplace= True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "ML_X = ML_X.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 52)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ML_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [],
   "source": [
    "MLPredictors= Standardization_Dataset_ML(ML_X,x_scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predictors_Lasso = ML_Lasso.predict(MLPredictors)\n",
    "Predictors_Linear = ML_Linear.predict(MLPredictors)\n",
    "Predictors_Ridge = ML_Ridge.predict(MLPredictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_Lasso = pd.DataFrame(Predictors_Lasso, columns = ['Lasso'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predictors_Lasso=pd.Series(Predictors_Lasso) \n",
    "Predictors_Linear=pd.Series(Predictors_Linear)\n",
    "Predictors_Ridge=pd.Series(Predictors_Ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(Predictors_Lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ML_dataframe(dd,forecast_series,Name):\n",
    "    Dataframe = dd\n",
    "    Dataframe[Name] = forecast_series\n",
    "    \n",
    "    return (Dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF_Lasso=ML_dataframe(dd,Predictors_Lasso,'Lasso')\n",
    "DF_Ridge=ML_dataframe(dd,Predictors_Ridge,'Ridge')\n",
    "DF_Linear=ML_dataframe(dd,Predictors_Ridge,'Linear')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining each forecast in single dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "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>0</th>\n",
       "      <th>Arima</th>\n",
       "      <th>Lasso</th>\n",
       "      <th>Ridge</th>\n",
       "      <th>Linear</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>112.219398</td>\n",
       "      <td>56.760617</td>\n",
       "      <td>156.735607</td>\n",
       "      <td>160.368160</td>\n",
       "      <td>160.368160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>104.250496</td>\n",
       "      <td>56.214125</td>\n",
       "      <td>105.773852</td>\n",
       "      <td>99.637499</td>\n",
       "      <td>99.637499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>95.713753</td>\n",
       "      <td>56.133761</td>\n",
       "      <td>94.675768</td>\n",
       "      <td>92.004395</td>\n",
       "      <td>92.004395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>81.500710</td>\n",
       "      <td>56.373590</td>\n",
       "      <td>92.975599</td>\n",
       "      <td>89.958401</td>\n",
       "      <td>89.958401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>72.243660</td>\n",
       "      <td>55.959304</td>\n",
       "      <td>84.819437</td>\n",
       "      <td>84.234528</td>\n",
       "      <td>84.234528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>63.602482</td>\n",
       "      <td>55.925832</td>\n",
       "      <td>76.665164</td>\n",
       "      <td>79.223461</td>\n",
       "      <td>79.223461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>62.003933</td>\n",
       "      <td>55.856457</td>\n",
       "      <td>69.219727</td>\n",
       "      <td>75.438711</td>\n",
       "      <td>75.438711</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0      Arima       Lasso       Ridge      Linear\n",
       "Date                                                                 \n",
       "2019-01-01  112.219398  56.760617  156.735607  160.368160  160.368160\n",
       "2019-01-02  104.250496  56.214125  105.773852   99.637499   99.637499\n",
       "2019-01-03   95.713753  56.133761   94.675768   92.004395   92.004395\n",
       "2019-01-04   81.500710  56.373590   92.975599   89.958401   89.958401\n",
       "2019-01-05   72.243660  55.959304   84.819437   84.234528   84.234528\n",
       "2019-01-06   63.602482  55.925832   76.665164   79.223461   79.223461\n",
       "2019-01-07   62.003933  55.856457   69.219727   75.438711   75.438711"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CombinedResult= pd.merge(RNN_Pred, DF_Linear, left_index=True, right_index=True)\n",
    "CombinedResult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "CombinedResult= DF_Linear"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing each model in plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(13, 7))\n",
    "ax.plot(df_subset['2018-12-01' : '2018-12-31'].loc[:, 'price'], label='Real', linewidth=2)\n",
    "ax.plot(CombinedResult['Lasso'], label='Lasso', linewidth=2)\n",
    "ax.plot(CombinedResult['Arima'], label='Arima', linewidth=2)\n",
    "ax.plot(CombinedResult['Ridge'], label='Ridge', linewidth=2)\n",
    "ax.plot(CombinedResult['Linear'], label='Linear', linewidth=2)\n",
    "ax.set_ylabel('Price')\n",
    "ax.set_title('RNN Prediction on Test set', fontsize=16)\n",
    "ax.legend(loc='best', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('SidiEnv')",
   "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": "3.10.4"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b2c393c67395d0e752316f20ccd20174b50ae80cf1388e877249abdad040433f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}