qrapproaches_istanbul.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_istanbul_data_mean_sdsh = np.array(pd.read_csv('data/istanbul/istanbul_data_mean_sdsh.csv')['NUMBER_OF_VEHICLES'])[169:-3500]\n",
    "train_ar_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/ar_istanbul_data_mean_sdsh_train.csv\")[\"0\"])\n",
    "train_arma_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/arma_istanbul_data_mean_sdsh_train.csv\")[\"0\"])\n",
    "train_arima_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/arima_istanbul_data_mean_sdsh_train.csv\")[\"0\"])\n",
    "train_sarima_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/sarima_istanbul_data_mean_sdsh_train.csv\")[\"0\"])\n",
    "train_slstm_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/slstm_istanbul_data_mean_sdsh_train.csv\")[\"0\"])\n",
    "train_ssvr_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/ssvr_istanbul_data_mean_sdsh_train.csv\")[\"0\"])\n",
    "train_sxgboost_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/sxgboost_istanbul_data_mean_sdsh_train.csv\")[\"0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_istanbul_data_mean_sdsh = np.array(pd.read_csv('data/istanbul/istanbul_data_mean_sdsh.csv')['NUMBER_OF_VEHICLES'][-3500:])\n",
    "test_ar_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/ar_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_arma_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/arma_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_arima_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/arima_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_sarima_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/sarima_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_slstm_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/slstm_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_ssvr_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/ssvr_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_sxgboost_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/sxgboost_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_slstmarima_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/slstmarima_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_ssvrarima_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/ssvrarima_istanbul_data_mean_sdsh_test.csv\")[\"0\"])\n",
    "test_sxgboostarima_istanbul_data_mean_sdsh = np.array(pd.read_csv(\"point_forecasts/sxgboostarima_istanbul_data_mean_sdsh_test.csv\")[\"0\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Historical PI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_size = 3500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "residuals_ar = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_ar_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_arma = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_arma_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_arima = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_arima_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_sarima = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_sarima_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_slstm = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_slstm_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_ssvr = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_ssvr_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_sxgboost = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_sxgboost_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_slstmarima = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_slstmarima_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_ssvrarima = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_ssvrarima_istanbul_data_mean_sdsh).reshape((test_size,1))\n",
    "residuals_sxgboostarima = np.array(test_istanbul_data_mean_sdsh).reshape((test_size,1)) - np.array(test_sxgboostarima_istanbul_data_mean_sdsh).reshape((test_size,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3500, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals_sxgboostarima.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_corridor(corridor_size, error_series):\n",
    "    corridor = []\n",
    "    length = len(error_series) - corridor_size\n",
    "    for i in range(length):\n",
    "        errors_in_corridor = error_series[i:i+corridor_size]\n",
    "        ordered_errors_in_corridor = np.sort(errors_in_corridor, axis=0)\n",
    "        corridor.append({\"OrderedErrors\": ordered_errors_in_corridor})\n",
    "    return corridor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "corridor_size = 21\n",
    "corridor_ar = get_corridor(corridor_size, residuals_ar)\n",
    "corridor_arma = get_corridor(corridor_size, residuals_arma)\n",
    "corridor_arima = get_corridor(corridor_size, residuals_arima)\n",
    "corridor_sarima = get_corridor(corridor_size, residuals_sarima)\n",
    "corridor_slstm = get_corridor(corridor_size, residuals_slstm)\n",
    "corridor_ssvr = get_corridor(corridor_size, residuals_ssvr)\n",
    "corridor_sxgboost = get_corridor(corridor_size, residuals_sxgboost)\n",
    "corridor_slstmarima = get_corridor(corridor_size, residuals_slstmarima)\n",
    "corridor_ssvrarima = get_corridor(corridor_size, residuals_ssvrarima)\n",
    "corridor_sxgboostarima = get_corridor(corridor_size, residuals_sxgboostarima)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.26727274],\n",
       "       [ 2.50201109],\n",
       "       [ 3.29761181],\n",
       "       [ 5.33822468],\n",
       "       [ 8.45045662],\n",
       "       [ 8.53331846],\n",
       "       [ 9.16776521],\n",
       "       [ 9.85881031],\n",
       "       [10.3439013 ],\n",
       "       [10.54687307],\n",
       "       [13.79061758],\n",
       "       [13.83879406],\n",
       "       [14.32114083],\n",
       "       [16.44284071],\n",
       "       [18.74244232],\n",
       "       [23.91188426],\n",
       "       [26.63452956],\n",
       "       [28.76314272],\n",
       "       [29.35488784],\n",
       "       [32.08997054],\n",
       "       [32.1918281 ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corridor_ar[0][\"OrderedErrors\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lower_upper_bounds(corridor_size, corridor, predictions):\n",
    "    PIs = []\n",
    "    predictions2 = np.array(predictions[corridor_size:])\n",
    "    percent5_index = 1\n",
    "    percent95_index = 19\n",
    "    for i in range(len(corridor)):\n",
    "        OrderedErrors = corridor[i][\"OrderedErrors\"]\n",
    "        PointForecast = predictions2[i]\n",
    "        lower_bound = OrderedErrors[percent5_index] + PointForecast\n",
    "        upper_bound = OrderedErrors[percent95_index] + PointForecast\n",
    "        PIs.append([lower_bound, upper_bound])\n",
    "    return PIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_PIs_ar = get_lower_upper_bounds(corridor_size, corridor_ar, test_ar_istanbul_data_mean_sdsh)\n",
    "historical_PIs_arma = get_lower_upper_bounds(corridor_size, corridor_arma, test_arma_istanbul_data_mean_sdsh)\n",
    "historical_PIs_arima = get_lower_upper_bounds(corridor_size, corridor_arima, test_arima_istanbul_data_mean_sdsh)\n",
    "historical_PIs_sarima = get_lower_upper_bounds(corridor_size, corridor_sarima, test_sarima_istanbul_data_mean_sdsh)\n",
    "historical_PIs_slstm = get_lower_upper_bounds(corridor_size, corridor_slstm, test_slstm_istanbul_data_mean_sdsh)\n",
    "historical_PIs_ssvr = get_lower_upper_bounds(corridor_size, corridor_ssvr, test_ssvr_istanbul_data_mean_sdsh)\n",
    "historical_PIs_sxgboost = get_lower_upper_bounds(corridor_size, corridor_sxgboost, test_sxgboost_istanbul_data_mean_sdsh)\n",
    "historical_PIs_slstmarima = get_lower_upper_bounds(corridor_size, corridor_slstmarima, test_slstmarima_istanbul_data_mean_sdsh)\n",
    "historical_PIs_ssvrarima = get_lower_upper_bounds(corridor_size, corridor_ssvrarima, test_ssvrarima_istanbul_data_mean_sdsh)\n",
    "historical_PIs_sxgboostarima = get_lower_upper_bounds(corridor_size, corridor_sxgboostarima, test_sxgboostarima_istanbul_data_mean_sdsh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3479"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(historical_PIs_ar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#implement onconditional coverage\n",
    "def unconditional_coverage(PIs, corridor_size, data_test):\n",
    "    data_test = np.array(data_test[corridor_size:])\n",
    "    indicator = []\n",
    "    for i in range(len(PIs)):\n",
    "        if PIs[i][0] < data_test[i] < PIs[i][1]:\n",
    "            indicator.append(1)\n",
    "        else:\n",
    "            indicator.append(0)\n",
    "    uc = np.sum(indicator)/len(indicator)\n",
    "    return uc\n",
    "\n",
    "#implement Winkler's score\n",
    "def winlers_score(PIs, corridor_size, data_test, tau):\n",
    "    data_test = np.array(data_test[corridor_size:])\n",
    "    indicators = []\n",
    "    for i in range(len(PIs)):\n",
    "        if PIs[i][0] < data_test[i] < PIs[i][1]:\n",
    "            score = PIs[i][1]-PIs[i][0]\n",
    "            indicators.append(score)\n",
    "        elif data_test[i] < PIs[i][0]:\n",
    "            score = (PIs[i][1]-PIs[i][0]) + (2 / tau * (PIs[i][0]-data_test[i]))\n",
    "            indicators.append(score)\n",
    "        elif PIs[i][1] < data_test[i]:\n",
    "            score = (PIs[i][1]-PIs[i][0]) + (2 / tau * (data_test[i]-PIs[i][1]))\n",
    "            indicators.append(score)\n",
    "    ws = np.sum(np.array(indicators))/len(indicators)\n",
    "    return ws\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8255245760275941\n",
      "0.8249496981891348\n",
      "0.7999425122161541\n",
      "0.8189134808853119\n",
      "0.8094279965507329\n",
      "0.8045415349238287\n",
      "0.8111526300661109\n",
      "0.8091405576315033\n",
      "0.8048289738430584\n",
      "0.8097154354699626\n"
     ]
    }
   ],
   "source": [
    "uc_ar = unconditional_coverage(historical_PIs_ar, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_arma = unconditional_coverage(historical_PIs_arma, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_arima = unconditional_coverage(historical_PIs_arima, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_sarima = unconditional_coverage(historical_PIs_sarima, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_slstm = unconditional_coverage(historical_PIs_slstm, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_ssvr = unconditional_coverage(historical_PIs_ssvr, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_sxgboost = unconditional_coverage(historical_PIs_sxgboost, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_slstmarima = unconditional_coverage(historical_PIs_slstmarima, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_ssvrarima = unconditional_coverage(historical_PIs_ssvrarima, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "uc_sxgboostarima = unconditional_coverage(historical_PIs_sxgboostarima, corridor_size, np.array(test_istanbul_data_mean_sdsh))\n",
    "print(uc_ar)\n",
    "print(uc_arma)\n",
    "print(uc_arima)\n",
    "print(uc_sarima)\n",
    "print(uc_slstm)\n",
    "print(uc_ssvr) \n",
    "print(uc_sxgboost)\n",
    "print(uc_slstmarima)\n",
    "print(uc_ssvrarima) \n",
    "print(uc_sxgboostarima)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tau = 0.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "321.1374026715215\n",
      "324.28346243074304\n",
      "373.31388823085774\n",
      "237.1241011050699\n",
      "174.92805885668437\n",
      "171.99301966386201\n",
      "179.94314102683262\n",
      "174.889312188422\n",
      "171.9497175858988\n",
      "180.07263168643715\n"
     ]
    }
   ],
   "source": [
    "winlers_score_ar = winlers_score(historical_PIs_ar, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_arma = winlers_score(historical_PIs_arma, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_arima = winlers_score(historical_PIs_arima, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_sarima = winlers_score(historical_PIs_sarima, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_slstm = winlers_score(historical_PIs_slstm, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_ssvr = winlers_score(historical_PIs_ssvr, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_sxgboost = winlers_score(historical_PIs_sxgboost, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_slstmarima = winlers_score(historical_PIs_slstmarima, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_ssvrarima = winlers_score(historical_PIs_ssvrarima, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "winlers_score_sxgboostarima = winlers_score(historical_PIs_sxgboostarima, corridor_size, np.array(test_istanbul_data_mean_sdsh), tau)\n",
    "\n",
    "print(winlers_score_ar)\n",
    "print(winlers_score_arma)\n",
    "print(winlers_score_arima)\n",
    "print(winlers_score_sarima)\n",
    "print(winlers_score_slstm)\n",
    "print(winlers_score_ssvr) \n",
    "print(winlers_score_sxgboost)\n",
    "print(winlers_score_slstmarima)\n",
    "print(winlers_score_ssvrarima) \n",
    "print(winlers_score_sxgboostarima)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distributional PIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "def armodel(train_data, test_data):\n",
    "    arima = ARIMA(train_data, order=(1,0,0))\n",
    "    arima_fit = arima.fit()    \n",
    "    print(arima_fit.summary())\n",
    "    parameters = arima_fit.params\n",
    "    a = parameters[1]\n",
    "    output_train = arima_fit.forecast()\n",
    "    train_predictions = []\n",
    "    for t in range(len(train_data)):\n",
    "        output_train = (train_data[t] * a)\n",
    "        train_predictions.append(output_train)\n",
    "        \n",
    "    output_test = arima_fit.forecast()\n",
    "    test_predictions = []\n",
    "    test_predictions.append(output_test[0][0])\n",
    "    for t in range(len(test_data)-1):\n",
    "        output_test = (test_data[t] * a)\n",
    "        test_predictions.append(output_test)\n",
    "    return train_predictions, test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/statsmodels/tsa/arima_model.py:472: FutureWarning: \n",
      "statsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\n",
      "been deprecated in favor of statsmodels.tsa.arima.model.ARIMA (note the .\n",
      "between arima and model) and\n",
      "statsmodels.tsa.SARIMAX. These will be removed after the 0.12 release.\n",
      "\n",
      "statsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\n",
      "is both well tested and maintained.\n",
      "\n",
      "To silence this warning and continue using ARMA and ARIMA until they are\n",
      "removed, use:\n",
      "\n",
      "import warnings\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARMA',\n",
      "                        FutureWarning)\n",
      "warnings.filterwarnings('ignore', 'statsmodels.tsa.arima_model.ARIMA',\n",
      "                        FutureWarning)\n",
      "\n",
      "  warnings.warn(ARIMA_DEPRECATION_WARN, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              ARMA Model Results                              \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   No. Observations:                 7995\n",
      "Model:                     ARMA(1, 0)   Log Likelihood              -44190.632\n",
      "Method:                       css-mle   S.D. of innovations             60.841\n",
      "Date:                Fri, 22 Jul 2022   AIC                          88387.263\n",
      "Time:                        19:21:30   BIC                          88408.223\n",
      "Sample:                             0   HQIC                         88394.437\n",
      "                                                                              \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        198.5299      4.485     44.264      0.000     189.739     207.321\n",
      "ar.L1.y        0.8484      0.006    143.280      0.000       0.837       0.860\n",
      "                                    Roots                                    \n",
      "=============================================================================\n",
      "                  Real          Imaginary           Modulus         Frequency\n",
      "-----------------------------------------------------------------------------\n",
      "AR.1            1.1787           +0.0000j            1.1787            0.0000\n",
      "-----------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "train_ar1_istanbul_data_mean_sdsh, test_ar1_istanbul_data_mean_sdsh = armodel(train_istanbul_data_mean_sdsh, test_istanbul_data_mean_sdsh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size = 7995\n",
    "residuals_ar_train = np.array(train_istanbul_data_mean_sdsh).reshape((train_size,1)) - np.array(train_ar1_istanbul_data_mean_sdsh).reshape((train_size,1))\n",
    "residuals_ar_test = residuals_ar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 0.8484\n",
    "sigma = np.std(residuals_ar_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_istanbul_data_mean_sdsh2 = []\n",
    "test_istanbul_data_mean_sdsh2.append(train_istanbul_data_mean_sdsh[-1])\n",
    "for i in range(len(test_istanbul_data_mean_sdsh)-1):\n",
    "    test_istanbul_data_mean_sdsh2.append(test_istanbul_data_mean_sdsh[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12.0, 12.0, 37.0, 67.0, 70.0]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_istanbul_data_mean_sdsh2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lower_upper_bounds(test_data2, a, sigma, z_alpha, z_1minalpha):\n",
    "    PIs = []\n",
    "    for i in range(len(test_data2)):\n",
    "        lower_bound = (a * test_data2[i]) + (sigma * z_alpha)\n",
    "        upper_bound = (a * test_data2[i]) + (sigma * z_1minalpha)\n",
    "        PIs.append([lower_bound, upper_bound])\n",
    "    return PIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_alpha = -1.6449\n",
    "z_1minalpha = 1.6449\n",
    "PIs_dist_based = get_lower_upper_bounds(test_istanbul_data_mean_sdsh2, a, sigma, z_alpha, z_1minalpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3500"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(PIs_dist_based)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unconditional_coverage(PIs, data_test):\n",
    "    indicator = []\n",
    "    for i in range(len(PIs)):\n",
    "        if PIs[i][0] < data_test[i] < PIs[i][1]:\n",
    "            indicator.append(1)\n",
    "        else:\n",
    "            indicator.append(0)\n",
    "    uc = np.sum(indicator)/len(indicator)\n",
    "    return uc\n",
    "\n",
    "def winlers_score(PIs, data_test, tau):\n",
    "    data_test = np.array(data_test)\n",
    "    indicators = []\n",
    "    for i in range(len(PIs)):\n",
    "        if PIs[i][0] < data_test[i] < PIs[i][1]:\n",
    "            score = PIs[i][1]-PIs[i][0]\n",
    "            indicators.append(score)\n",
    "        elif data_test[i] < PIs[i][0]:\n",
    "            score = (PIs[i][1]-PIs[i][0]) + (2 / tau * (PIs[i][0]-data_test[i]))\n",
    "            indicators.append(score)\n",
    "        elif PIs[i][1] < data_test[i]:\n",
    "            score = (PIs[i][1]-PIs[i][0]) + (2 / tau * (data_test[i]-PIs[i][1]))\n",
    "            indicators.append(score)\n",
    "    ws = np.sum(np.array(indicators))/len(indicators)\n",
    "    return ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5114285714285715\n"
     ]
    }
   ],
   "source": [
    "uc_distbased = unconditional_coverage(PIs_dist_based, test_istanbul_data_mean_sdsh)\n",
    "print(uc_distbased)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "523.2311826171043\n"
     ]
    }
   ],
   "source": [
    "ws_distbased = winlers_score(PIs_dist_based, test_istanbul_data_mean_sdsh, tau=0.10)\n",
    "print(ws_distbased)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QRA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def qra(train_dataframe, test_dataframe):\n",
    "    tau1=0.95\n",
    "    tau2=0.05\n",
    "    #since the best 3 models are ssvr, slstm and sxgboost\n",
    "    #we use these models in QRA.\n",
    "    model1 = smf.quantreg('NUMBER_OF_VEHICLES ~ ssvr + slstm + sxgboost', train_dataframe).fit(q=tau1)\n",
    "    get_y = lambda a, b, c, d: a + b * test_dataframe.ssvr + c * test_dataframe.slstm + d * test_dataframe.sxgboost\n",
    "    y_upper = get_y(model1.params['Intercept'], model1.params['ssvr'], model1.params['slstm'], model1.params['sxgboost'])\n",
    "    model2 = smf.quantreg('NUMBER_OF_VEHICLES ~ ssvr + slstm + sxgboost', train_dataframe).fit(q=tau2)\n",
    "    y_lower = get_y(model2.params['Intercept'], model2.params['ssvr'], model2.params['slstm'], model1.params['sxgboost'])\n",
    "    y_upper = np.array(y_upper)\n",
    "    y_lower = np.array(y_lower)\n",
    "\n",
    "    PIs_qra = []\n",
    "    for i in range(len(y_upper)):\n",
    "        PIs_qra.append([y_lower[i], y_upper[i]])\n",
    "    return PIs_qra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>ssvr</th>\n",
       "      <th>slstm</th>\n",
       "      <th>sxgboost</th>\n",
       "      <th>NUMBER_OF_VEHICLES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87.141788</td>\n",
       "      <td>94.181190</td>\n",
       "      <td>91.612350</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63.788411</td>\n",
       "      <td>60.602642</td>\n",
       "      <td>61.982390</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.828216</td>\n",
       "      <td>45.230820</td>\n",
       "      <td>46.474990</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.473491</td>\n",
       "      <td>48.627926</td>\n",
       "      <td>48.241646</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101.861110</td>\n",
       "      <td>70.496140</td>\n",
       "      <td>113.158775</td>\n",
       "      <td>128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7990</th>\n",
       "      <td>72.715350</td>\n",
       "      <td>60.245995</td>\n",
       "      <td>71.897770</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7991</th>\n",
       "      <td>39.283531</td>\n",
       "      <td>28.279072</td>\n",
       "      <td>43.158688</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7992</th>\n",
       "      <td>28.373605</td>\n",
       "      <td>33.883450</td>\n",
       "      <td>28.377210</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7993</th>\n",
       "      <td>23.720291</td>\n",
       "      <td>18.630350</td>\n",
       "      <td>24.615215</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7994</th>\n",
       "      <td>18.957397</td>\n",
       "      <td>18.185514</td>\n",
       "      <td>16.128706</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7995 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ssvr      slstm    sxgboost  NUMBER_OF_VEHICLES\n",
       "0      87.141788  94.181190   91.612350                71.0\n",
       "1      63.788411  60.602642   61.982390                56.0\n",
       "2      42.828216  45.230820   46.474990                44.0\n",
       "3      46.473491  48.627926   48.241646                55.0\n",
       "4     101.861110  70.496140  113.158775               128.0\n",
       "...          ...        ...         ...                 ...\n",
       "7990   72.715350  60.245995   71.897770                44.0\n",
       "7991   39.283531  28.279072   43.158688                49.0\n",
       "7992   28.373605  33.883450   28.377210                17.0\n",
       "7993   23.720291  18.630350   24.615215                14.0\n",
       "7994   18.957397  18.185514   16.128706                12.0\n",
       "\n",
       "[7995 rows x 4 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_istanbul_data_mean_sdsh2 = pd.DataFrame(train_istanbul_data_mean_sdsh.reshape((len(train_istanbul_data_mean_sdsh),1)))\n",
    "train_ssvr_istanbul_data_mean_sdsh2 = pd.DataFrame(train_ssvr_istanbul_data_mean_sdsh.reshape((len(train_istanbul_data_mean_sdsh),1)))\n",
    "train_slstm_istanbul_data_mean_sdsh2 = pd.DataFrame(train_slstm_istanbul_data_mean_sdsh.reshape((len(train_istanbul_data_mean_sdsh),1)))\n",
    "train_sxgboost_istanbul_data_mean_sdsh2 = pd.DataFrame(train_sxgboost_istanbul_data_mean_sdsh.reshape((len(train_istanbul_data_mean_sdsh),1)))\n",
    "\n",
    "train_dataframe_qra = pd.concat([train_ssvr_istanbul_data_mean_sdsh2,train_slstm_istanbul_data_mean_sdsh2,train_sxgboost_istanbul_data_mean_sdsh2, train_istanbul_data_mean_sdsh2], axis=1)\n",
    "train_dataframe_qra.columns = [\"ssvr\",\"slstm\",\"sxgboost\",\"NUMBER_OF_VEHICLES\"]\n",
    "train_dataframe_qra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>ssvr</th>\n",
       "      <th>slstm</th>\n",
       "      <th>sxgboost</th>\n",
       "      <th>NUMBER_OF_VEHICLES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.367457</td>\n",
       "      <td>17.964338</td>\n",
       "      <td>25.924292</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32.975584</td>\n",
       "      <td>27.827078</td>\n",
       "      <td>35.199562</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57.977768</td>\n",
       "      <td>61.811943</td>\n",
       "      <td>65.214455</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.603856</td>\n",
       "      <td>71.906340</td>\n",
       "      <td>75.424000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77.803649</td>\n",
       "      <td>88.110990</td>\n",
       "      <td>85.941940</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3495</th>\n",
       "      <td>212.025030</td>\n",
       "      <td>205.218780</td>\n",
       "      <td>204.906770</td>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3496</th>\n",
       "      <td>152.838606</td>\n",
       "      <td>172.315870</td>\n",
       "      <td>165.084840</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3497</th>\n",
       "      <td>128.176479</td>\n",
       "      <td>137.565950</td>\n",
       "      <td>116.670580</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3498</th>\n",
       "      <td>121.188584</td>\n",
       "      <td>127.346780</td>\n",
       "      <td>126.039210</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3499</th>\n",
       "      <td>107.188718</td>\n",
       "      <td>110.663440</td>\n",
       "      <td>84.733246</td>\n",
       "      <td>146.793651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3500 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ssvr       slstm    sxgboost  NUMBER_OF_VEHICLES\n",
       "0      21.367457   17.964338   25.924292           12.000000\n",
       "1      32.975584   27.827078   35.199562           37.000000\n",
       "2      57.977768   61.811943   65.214455           67.000000\n",
       "3      67.603856   71.906340   75.424000           70.000000\n",
       "4      77.803649   88.110990   85.941940           83.000000\n",
       "...          ...         ...         ...                 ...\n",
       "3495  212.025030  205.218780  204.906770          198.000000\n",
       "3496  152.838606  172.315870  165.084840          139.000000\n",
       "3497  128.176479  137.565950  116.670580          128.000000\n",
       "3498  121.188584  127.346780  126.039210          109.000000\n",
       "3499  107.188718  110.663440   84.733246          146.793651\n",
       "\n",
       "[3500 rows x 4 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_istanbul_data_mean_sdsh2 = pd.DataFrame(test_istanbul_data_mean_sdsh.reshape((len(test_istanbul_data_mean_sdsh),1)))\n",
    "test_ssvr_istanbul_data_mean_sdsh2 = pd.DataFrame(test_ssvr_istanbul_data_mean_sdsh.reshape((len(test_istanbul_data_mean_sdsh),1)))\n",
    "test_slstm_istanbul_data_mean_sdsh2 = pd.DataFrame(test_slstm_istanbul_data_mean_sdsh.reshape((len(test_istanbul_data_mean_sdsh),1)))\n",
    "test_sxgboost_istanbul_data_mean_sdsh2 = pd.DataFrame(test_sxgboost_istanbul_data_mean_sdsh.reshape((len(test_istanbul_data_mean_sdsh),1)))\n",
    "\n",
    "test_dataframe_qra = pd.concat([test_ssvr_istanbul_data_mean_sdsh2,test_slstm_istanbul_data_mean_sdsh2,test_sxgboost_istanbul_data_mean_sdsh2, test_istanbul_data_mean_sdsh2], axis=1)\n",
    "test_dataframe_qra.columns = [\"ssvr\",\"slstm\",\"sxgboost\",\"NUMBER_OF_VEHICLES\"]\n",
    "test_dataframe_qra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3500"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PIS_qra = qra(train_dataframe_qra, test_dataframe_qra)\n",
    "len(PIS_qra)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8174285714285714\n",
      "178.64644553127073\n"
     ]
    }
   ],
   "source": [
    "uc_qra = unconditional_coverage(PIS_qra, test_istanbul_data_mean_sdsh)\n",
    "print(uc_qra)\n",
    "ws_qra = winlers_score(PIS_qra, test_istanbul_data_mean_sdsh, tau=0.10)\n",
    "print(ws_qra)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QRLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/istanbul/istanbul_data_mean_sdsh.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>t-169</th>\n",
       "      <th>t-168</th>\n",
       "      <th>t-167</th>\n",
       "      <th>t-25</th>\n",
       "      <th>t-24</th>\n",
       "      <th>t-23</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.0</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11659</th>\n",
       "      <td>187.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>267.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11660</th>\n",
       "      <td>100.0</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>246.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11661</th>\n",
       "      <td>91.0</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11662</th>\n",
       "      <td>88.0</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>146.793651</td>\n",
       "      <td>156.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11663</th>\n",
       "      <td>106.0</td>\n",
       "      <td>146.793651</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>136.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>146.793651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11664 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       t-169       t-168       t-167   t-25   t-24   t-23    t-2    t-1  \\\n",
       "0        NaN         NaN         NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1        NaN         NaN         NaN    NaN    NaN    NaN    NaN  114.0   \n",
       "2        NaN         NaN         NaN    NaN    NaN    NaN  114.0  105.0   \n",
       "3        NaN         NaN         NaN    NaN    NaN    NaN  105.0   97.0   \n",
       "4        NaN         NaN         NaN    NaN    NaN    NaN   97.0   63.0   \n",
       "...      ...         ...         ...    ...    ...    ...    ...    ...   \n",
       "11659  187.0  100.000000   91.000000  267.0  246.0  169.0  351.0  273.0   \n",
       "11660  100.0   91.000000   88.000000  246.0  169.0  156.0  273.0  198.0   \n",
       "11661   91.0   88.000000  106.000000  169.0  156.0  136.0  198.0  139.0   \n",
       "11662   88.0  106.000000  146.793651  156.0  136.0   87.0  139.0  128.0   \n",
       "11663  106.0  146.793651  100.000000  136.0   87.0  108.0  128.0  109.0   \n",
       "\n",
       "                t  \n",
       "0      114.000000  \n",
       "1      105.000000  \n",
       "2       97.000000  \n",
       "3       63.000000  \n",
       "4       53.000000  \n",
       "...           ...  \n",
       "11659  198.000000  \n",
       "11660  139.000000  \n",
       "11661  128.000000  \n",
       "11662  109.000000  \n",
       "11663  146.793651  \n",
       "\n",
       "[11664 rows x 9 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It is for SVR -----> SVRARIMA\n",
    "data2 = data[['NUMBER_OF_VEHICLES']]\n",
    "data3 = pd.DataFrame(data2.values)\n",
    "data4 = pd.concat([data3.shift(169),data3.shift(168),data3.shift(167),data3.shift(25),data3.shift(24),data3.shift(23),data3.shift(2),data3.shift(1), data3], axis=1)\n",
    "data4.columns = ['t-169','t-168','t-167','t-25','t-24','t-23','t-2','t-1', 't']\n",
    "data4.tail(100)\n",
    "X = data4.values\n",
    "train_size = int(len(X) * 0.70)\n",
    "data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11495, 8)\n",
      "(11495,)\n",
      "torch.Size([11495, 8, 1])\n",
      "torch.Size([11495, 1])\n",
      "torch.Size([7995, 8, 1])\n",
      "torch.Size([7995, 1])\n",
      "torch.Size([3500, 8, 1])\n",
      "torch.Size([3500, 1])\n"
     ]
    }
   ],
   "source": [
    "sc = MinMaxScaler()\n",
    "training_data = sc.fit_transform(X)\n",
    "\n",
    "seq_length = 1\n",
    "x, y = training_data[169:,:8], training_data[169:,-1]\n",
    "print(x.shape)\n",
    "print(y.shape)\n",
    "train_size = 7995\n",
    "\n",
    "\n",
    "dataX = Variable(torch.Tensor(np.array(x))).reshape((11495,8,1))\n",
    "dataY = Variable(torch.Tensor(np.array(y))).reshape((11495,1))\n",
    "\n",
    "trainX = Variable(torch.Tensor(np.array(x[0:train_size]))).reshape((7995,8,1))\n",
    "trainY = Variable(torch.Tensor(np.array(y[0:train_size]))).reshape((7995,1))\n",
    "\n",
    "testX = Variable(torch.Tensor(np.array(x[train_size:]))).reshape((3500,8,1))\n",
    "testY = Variable(torch.Tensor(np.array(y[train_size:]))).reshape((3500,1))\n",
    "\n",
    "print(dataX.shape)\n",
    "print(dataY.shape)\n",
    "print(trainX.shape)\n",
    "print(trainY.shape)\n",
    "print(testX.shape)\n",
    "print(testY.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    \n",
    "    def __init__(self, num_classes, input_size, hidden_size, num_layers):\n",
    "        super(LSTM, self).__init__()\n",
    "        \n",
    "        self.num_classes = num_classes\n",
    "        self.num_layers = num_layers\n",
    "        self.input_size = input_size\n",
    "        self.hidden_size = hidden_size\n",
    "        self.seq_length = seq_length\n",
    "        \n",
    "        self.lstm = nn.LSTM(input_size=input_size, hidden_size=hidden_size,\n",
    "                            num_layers=num_layers, batch_first=True)\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        self.fc = nn.Linear(hidden_size, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        h_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size))\n",
    "        c_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size))\n",
    "        _, (h_output, _) = self.lstm(x, (h_0, c_0))\n",
    "        h_output = h_output.view(-1, self.hidden_size)\n",
    "        output = self.softmax(h_output)\n",
    "        output = self.fc(output)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PinballLoss():\n",
    "    def __init__(self, quantile=0.10, reduction='mean'):\n",
    "        self.quantile = quantile\n",
    "        assert 0 < self.quantile\n",
    "        assert self.quantile < 1\n",
    "        self.reduction = reduction\n",
    "    def __call__(self, output, target):\n",
    "        assert output.shape == target.shape\n",
    "        loss = torch.zeros_like(target, dtype=torch.float)\n",
    "        error = output - target \n",
    "        smaller_index = error < 0\n",
    "        bigger_index = 0 < error\n",
    "        loss[smaller_index] = self.quantile * (abs(error)[smaller_index])\n",
    "        loss[bigger_index] = (1-self.quantile) * (abs(error)[bigger_index])\n",
    "        \n",
    "        if self.reduction == 'sum':\n",
    "            loss = loss.sum()\n",
    "        if self.reduction == 'mean':\n",
    "            loss = loss.mean()\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, loss: 0.26817\n",
      "Epoch: 100, loss: 0.01788\n",
      "Epoch: 200, loss: 0.01782\n",
      "Epoch: 300, loss: 0.01771\n",
      "Epoch: 400, loss: 0.01741\n",
      "Epoch: 500, loss: 0.01451\n",
      "Epoch: 600, loss: 0.01336\n",
      "Epoch: 700, loss: 0.01210\n",
      "Epoch: 800, loss: 0.01007\n",
      "Epoch: 900, loss: 0.00916\n",
      "Epoch: 1000, loss: 0.00810\n",
      "Epoch: 1100, loss: 0.00779\n",
      "Epoch: 1200, loss: 0.00735\n",
      "Epoch: 1300, loss: 0.00722\n",
      "Epoch: 1400, loss: 0.00696\n",
      "Epoch: 1500, loss: 0.00686\n",
      "Epoch: 1600, loss: 0.00676\n",
      "Epoch: 1700, loss: 0.00673\n",
      "Epoch: 1800, loss: 0.00665\n",
      "Epoch: 1900, loss: 0.00661\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 2000\n",
    "learning_rate = 0.01\n",
    "\n",
    "input_size = 1\n",
    "hidden_size = 8\n",
    "num_layers = 1\n",
    "num_classes = 1\n",
    "\n",
    "lstm = LSTM(num_classes, input_size, hidden_size, num_layers)\n",
    "criterion = PinballLoss(quantile=0.95)\n",
    "optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate)\n",
    "for epoch in range(num_epochs):\n",
    "    outputs = lstm(trainX)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, trainY)\n",
    "    loss.backward()\n",
    "    \n",
    "    optimizer.step()\n",
    "    if epoch % 100 == 0:\n",
    "      print(\"Epoch: %d, loss: %1.5f\" % (epoch, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3500, 1])\n",
      "(3500, 1)\n",
      "(11495, 1)\n"
     ]
    }
   ],
   "source": [
    "lstm.eval()\n",
    "test_predict = lstm(testX)\n",
    "print(test_predict.shape)\n",
    "\n",
    "data_predict = test_predict.data.numpy()\n",
    "dataY_plot = dataY.data.numpy()\n",
    "print(data_predict.shape)\n",
    "print(dataY_plot.shape)\n",
    "\n",
    "d_p = np.concatenate((data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict),axis=1)\n",
    "dY_p = np.concatenate((dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot),axis=1)\n",
    "data_predict = sc.inverse_transform(d_p)\n",
    "dataY_plot = sc.inverse_transform(dY_p)\n",
    "\n",
    "dataY_plot = dataY_plot[:,0]\n",
    "data_predict = data_predict[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3500"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_bound = data_predict\n",
    "len(upper_bound)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, loss: 0.02123\n",
      "Epoch: 100, loss: 0.00817\n",
      "Epoch: 200, loss: 0.00729\n",
      "Epoch: 300, loss: 0.00712\n",
      "Epoch: 400, loss: 0.00689\n",
      "Epoch: 500, loss: 0.00666\n",
      "Epoch: 600, loss: 0.00671\n",
      "Epoch: 700, loss: 0.00661\n",
      "Epoch: 800, loss: 0.00656\n",
      "Epoch: 900, loss: 0.00654\n",
      "Epoch: 1000, loss: 0.00682\n",
      "Epoch: 1100, loss: 0.00648\n",
      "Epoch: 1200, loss: 0.00644\n",
      "Epoch: 1300, loss: 0.00640\n",
      "Epoch: 1400, loss: 0.00634\n",
      "Epoch: 1500, loss: 0.00632\n",
      "Epoch: 1600, loss: 0.00627\n",
      "Epoch: 1700, loss: 0.00625\n",
      "Epoch: 1800, loss: 0.00622\n",
      "Epoch: 1900, loss: 0.00620\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 2000\n",
    "learning_rate = 0.01\n",
    "\n",
    "input_size = 1\n",
    "hidden_size = 8\n",
    "num_layers = 1\n",
    "num_classes = 1\n",
    "\n",
    "lstm = LSTM(num_classes, input_size, hidden_size, num_layers)\n",
    "criterion = PinballLoss(quantile=0.05)\n",
    "optimizer = torch.optim.Adam(lstm.parameters(), lr=learning_rate)\n",
    "# Train the model\n",
    "for epoch in range(num_epochs):\n",
    "    outputs = lstm(trainX)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, trainY)\n",
    "    loss.backward()\n",
    "    \n",
    "    optimizer.step()\n",
    "    if epoch % 100 == 0:\n",
    "      print(\"Epoch: %d, loss: %1.5f\" % (epoch, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3500, 1])\n",
      "(3500, 1)\n",
      "(11495, 1)\n"
     ]
    }
   ],
   "source": [
    "lstm.eval()\n",
    "test_predict = lstm(testX)\n",
    "print(test_predict.shape)\n",
    "\n",
    "data_predict = test_predict.data.numpy()\n",
    "dataY_plot = dataY.data.numpy()\n",
    "print(data_predict.shape)\n",
    "print(dataY_plot.shape)\n",
    "\n",
    "d_p = np.concatenate((data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict),axis=1)\n",
    "dY_p = np.concatenate((dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot),axis=1)\n",
    "data_predict = sc.inverse_transform(d_p)\n",
    "dataY_plot = sc.inverse_transform(dY_p)\n",
    "\n",
    "dataY_plot = dataY_plot[:,0]\n",
    "data_predict = data_predict[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bound = data_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_upper = np.array(upper_bound)\n",
    "y_lower = np.array(lower_bound)\n",
    "\n",
    "PIs_qrlstm = []\n",
    "for i in range(len(y_upper)):\n",
    "    PIs_qrlstm.append([y_lower[i], y_upper[i]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3500"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = dataY_plot[7995:]\n",
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8925714285714286\n",
      "142.9475911238534\n"
     ]
    }
   ],
   "source": [
    "uc_qrlstm = unconditional_coverage(PIs_qrlstm, test_data)\n",
    "print(uc_qrlstm)\n",
    "ws_qrlstm = winlers_score(PIs_qrlstm, test_data, tau=0.10)\n",
    "print(ws_qrlstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[8.0172831094722, 46.305555373819864],\n",
       " [14.67418848718544, 56.10759315215689],\n",
       " [40.682606151541776, 89.11495178151165],\n",
       " [49.42692926793961, 100.50118531756661],\n",
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       " [23.124065214774525, 68.10213781388263],\n",
       " [54.2838483461845, 108.9371012123081],\n",
       " [147.07060639367307, 219.578704212589],\n",
       " [163.0315524374907, 244.42269198030328],\n",
       " [168.79501440186917, 255.66500977604628],\n",
       " [182.34694198132138, 271.57709297167315],\n",
       " [167.88660882329765, 260.70191698503004],\n",
       " [144.50768980209185, 229.70001648590468],\n",
       " [139.81231000329439, 224.74303148693525],\n",
       " [136.11373417067728, 217.76812740519543],\n",
       " [143.62755841791238, 225.31546102516006],\n",
       " [140.60636920087904, 224.20545428142208],\n",
       " [132.07638081973369, 206.2832426882724],\n",
       " [128.84225160999662, 205.5295346664817],\n",
       " [123.29222384124881, 205.55973440960923],\n",
       " [102.26845398210918, 180.4206997581251],\n",
       " ...]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PIS_qra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " [28.380804, 57.979424],\n",
       " [15.558193, 42.070263],\n",
       " [18.566362, 48.107166],\n",
       " [36.84598, 65.480515],\n",
       " [89.77113, 206.06267],\n",
       " [245.26363, 439.4024],\n",
       " [336.42944, 508.91464],\n",
       " [303.97012, 505.28696],\n",
       " [300.56003, 506.02185],\n",
       " [262.73563, 427.06638],\n",
       " [257.41705, 377.26813],\n",
       " [258.94226, 377.0315],\n",
       " [235.38942, 350.06644],\n",
       " [258.18613, 371.72052],\n",
       " [249.40826, 365.0261],\n",
       " [196.76825, 318.9491],\n",
       " [237.08595, 465.8292],\n",
       " [289.68365, 440.28152],\n",
       " [260.64468, 397.12234],\n",
       " [196.67683, 340.21442],\n",
       " [138.69266, 289.46234],\n",
       " [72.85449, 178.4168],\n",
       " [60.713493, 115.677086],\n",
       " [39.969414, 81.375145],\n",
       " [32.630325, 66.003334],\n",
       " [24.187134, 53.4598],\n",
       " [23.182878, 52.04849],\n",
       " [38.146896, 68.96477],\n",
       " [78.14462, 187.7737],\n",
       " [275.23715, 441.8208],\n",
       " [312.0144, 512.166],\n",
       " [316.66272, 508.48303],\n",
       " [303.18884, 503.6443],\n",
       " [269.93863, 436.83743],\n",
       " [246.99086, 369.91354],\n",
       " [243.7353, 357.6916],\n",
       " [250.4168, 364.35602],\n",
       " [240.04495, 353.68222],\n",
       " [204.97903, 333.06104],\n",
       " [216.49529, 350.57236],\n",
       " [217.56458, 362.4227],\n",
       " [234.21353, 386.5274],\n",
       " [264.2298, 398.06348],\n",
       " [187.26437, 329.19394],\n",
       " [152.25801, 299.36703],\n",
       " [86.659744, 199.58717],\n",
       " [67.95662, 125.651794],\n",
       " [52.401245, 100.462845],\n",
       " [37.90694, 73.24751],\n",
       " [26.639719, 55.057384],\n",
       " [22.70274, 53.001945],\n",
       " [36.62474, 63.26259],\n",
       " [87.82007, 226.35252],\n",
       " [259.88397, 443.81982],\n",
       " [336.31125, 511.57947],\n",
       " [300.57098, 504.75543],\n",
       " [258.4539, 487.1487],\n",
       " [284.36566, 439.29483],\n",
       " [281.3208, 398.12067],\n",
       " [260.59198, 381.96564],\n",
       " [248.36685, 365.57245],\n",
       " [235.87695, 367.2305],\n",
       " [159.27448, 283.37885],\n",
       " [171.60094, 283.02844],\n",
       " [242.90985, 358.41626],\n",
       " [229.94286, 343.4432],\n",
       " [234.0856, 354.88953],\n",
       " [225.98587, 351.59534],\n",
       " [151.8399, 309.45633],\n",
       " [85.048676, 197.94421],\n",
       " [84.49504, 143.47026],\n",
       " [54.96941, 117.45489],\n",
       " [35.60138, 65.80994],\n",
       " [23.948774, 52.09144],\n",
       " [20.673286, 47.267715],\n",
       " [32.39088, 62.930607],\n",
       " [51.197937, 144.25629],\n",
       " [107.58253, 245.47035],\n",
       " [123.327995, 280.9996],\n",
       " [158.07681, 310.46613],\n",
       " [156.18832, 299.9295],\n",
       " [167.04657, 308.55307],\n",
       " [145.77348, 271.13226],\n",
       " [147.30753, 256.30914],\n",
       " [128.68404, 225.72324],\n",
       " [146.37251, 224.09384],\n",
       " [130.02603, 218.78505],\n",
       " [95.00631, 190.12349],\n",
       " [122.33971, 203.01102],\n",
       " [134.83609, 232.52339],\n",
       " [121.23556, 228.94896],\n",
       " ...]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PIs_qrlstm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "PIs_qra_xs = []\n",
    "PIs_qra_ys = []\n",
    "for i in range(len(PIS_qra)):\n",
    "    PIs_qra_xs.append(PIS_qra[i][0])\n",
    "    PIs_qra_ys.append(PIS_qra[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = np.arange(100)\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.fill_between(time, PIs_qra_xs[:100], PIs_qra_ys[:100], color=\"pink\")\n",
    "plt.plot(test_data[:100], marker=\"o\", color=\"black\")\n",
    "plt.ylabel(\"Traffic Flow\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "PIs_qrlstm_xs = []\n",
    "PIs_qrlstm_ys = []\n",
    "for i in range(len(PIs_qrlstm)):\n",
    "    PIs_qrlstm_xs.append(PIs_qrlstm[i][0])\n",
    "    PIs_qrlstm_ys.append(PIs_qrlstm[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = np.arange(100)\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.fill_between(time, PIs_qrlstm_xs[:100], PIs_qrlstm_ys[:100], color=\"pink\")\n",
    "plt.plot(test_data[:100], marker=\"o\", color=\"black\")\n",
    "plt.ylabel(\"Traffic Flow\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QRNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.autograd import Variable\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/istanbul/istanbul_data_mean_sdsh.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>t-169</th>\n",
       "      <th>t-168</th>\n",
       "      <th>t-167</th>\n",
       "      <th>t-25</th>\n",
       "      <th>t-24</th>\n",
       "      <th>t-23</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.0</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>114.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>97.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>63.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11659</th>\n",
       "      <td>187.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>267.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11660</th>\n",
       "      <td>100.0</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>246.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11661</th>\n",
       "      <td>91.0</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>169.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11662</th>\n",
       "      <td>88.0</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>146.793651</td>\n",
       "      <td>156.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11663</th>\n",
       "      <td>106.0</td>\n",
       "      <td>146.793651</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>136.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>146.793651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11664 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       t-169       t-168       t-167   t-25   t-24   t-23    t-2    t-1  \\\n",
       "0        NaN         NaN         NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1        NaN         NaN         NaN    NaN    NaN    NaN    NaN  114.0   \n",
       "2        NaN         NaN         NaN    NaN    NaN    NaN  114.0  105.0   \n",
       "3        NaN         NaN         NaN    NaN    NaN    NaN  105.0   97.0   \n",
       "4        NaN         NaN         NaN    NaN    NaN    NaN   97.0   63.0   \n",
       "...      ...         ...         ...    ...    ...    ...    ...    ...   \n",
       "11659  187.0  100.000000   91.000000  267.0  246.0  169.0  351.0  273.0   \n",
       "11660  100.0   91.000000   88.000000  246.0  169.0  156.0  273.0  198.0   \n",
       "11661   91.0   88.000000  106.000000  169.0  156.0  136.0  198.0  139.0   \n",
       "11662   88.0  106.000000  146.793651  156.0  136.0   87.0  139.0  128.0   \n",
       "11663  106.0  146.793651  100.000000  136.0   87.0  108.0  128.0  109.0   \n",
       "\n",
       "                t  \n",
       "0      114.000000  \n",
       "1      105.000000  \n",
       "2       97.000000  \n",
       "3       63.000000  \n",
       "4       53.000000  \n",
       "...           ...  \n",
       "11659  198.000000  \n",
       "11660  139.000000  \n",
       "11661  128.000000  \n",
       "11662  109.000000  \n",
       "11663  146.793651  \n",
       "\n",
       "[11664 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It is for SVR -----> SVRARIMA\n",
    "data2 = data[['NUMBER_OF_VEHICLES']]\n",
    "data3 = pd.DataFrame(data2.values)\n",
    "data4 = pd.concat([data3.shift(169),data3.shift(168),data3.shift(167),data3.shift(25),data3.shift(24),data3.shift(23),data3.shift(2),data3.shift(1), data3], axis=1)\n",
    "data4.columns = ['t-169','t-168','t-167','t-25','t-24','t-23','t-2','t-1', 't']\n",
    "data4.tail(100)\n",
    "X = data4.values\n",
    "train_size = int(len(X) * 0.70)\n",
    "data4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11495, 8)\n",
      "(11495,)\n",
      "torch.Size([11495, 8])\n",
      "torch.Size([11495, 1])\n",
      "torch.Size([7995, 8])\n",
      "torch.Size([7995, 1])\n",
      "torch.Size([3500, 8])\n",
      "torch.Size([3500, 1])\n"
     ]
    }
   ],
   "source": [
    "sc = MinMaxScaler()\n",
    "training_data = sc.fit_transform(X)\n",
    "\n",
    "seq_length = 1\n",
    "x, y = training_data[169:,:8], training_data[169:,-1]\n",
    "print(x.shape)\n",
    "print(y.shape)\n",
    "train_size = 7995\n",
    "\n",
    "\n",
    "dataX = Variable(torch.Tensor(np.array(x))).reshape((11495,8))\n",
    "dataY = Variable(torch.Tensor(np.array(y))).reshape((11495,1))\n",
    "\n",
    "trainX = Variable(torch.Tensor(np.array(x[0:train_size]))).reshape((7995,8))\n",
    "trainY = Variable(torch.Tensor(np.array(y[0:train_size]))).reshape((7995,1))\n",
    "\n",
    "testX = Variable(torch.Tensor(np.array(x[train_size:]))).reshape((3500,8))\n",
    "testY = Variable(torch.Tensor(np.array(y[train_size:]))).reshape((3500,1))\n",
    "\n",
    "print(dataX.shape)\n",
    "print(dataY.shape)\n",
    "print(trainX.shape)\n",
    "print(trainY.shape)\n",
    "print(testX.shape)\n",
    "print(testY.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PinballLoss():\n",
    "    def __init__(self, quantile=0.10, reduction='mean'):\n",
    "        self.quantile = quantile\n",
    "        assert 0 < self.quantile\n",
    "        assert self.quantile < 1\n",
    "        self.reduction = reduction\n",
    "    def __call__(self, output, target):\n",
    "        assert output.shape == target.shape\n",
    "        loss = torch.zeros_like(target, dtype=torch.float)\n",
    "        error = output - target \n",
    "        smaller_index = error < 0\n",
    "        bigger_index = 0 < error\n",
    "        loss[smaller_index] = self.quantile * (abs(error)[smaller_index])\n",
    "        loss[bigger_index] = (1-self.quantile) * (abs(error)[bigger_index])\n",
    "        \n",
    "        if self.reduction == 'sum':\n",
    "            loss = loss.sum()\n",
    "        if self.reduction == 'mean':\n",
    "            loss = loss.mean()\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ANN(nn.Module):\n",
    "   def __init__(self):\n",
    "      super(ANN, self).__init__()\n",
    "      self.fc1 = nn.Linear(8, 20)\n",
    "      self.fc2 = nn.Linear(20, 1)\n",
    "   def forward(self, x):\n",
    "      x = F.tanh(self.fc1(x))\n",
    "      x = F.sigmoid(self.fc2(x))\n",
    "      return x\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANN(\n",
      "  (fc1): Linear(in_features=8, out_features=20, bias=True)\n",
      "  (fc2): Linear(in_features=20, out_features=1, bias=True)\n",
      ")\n",
      "Epoch: 0, loss: 0.02970\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1639: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100, loss: 0.00885\n",
      "Epoch: 200, loss: 0.00728\n",
      "Epoch: 300, loss: 0.00668\n",
      "Epoch: 400, loss: 0.00640\n",
      "Epoch: 500, loss: 0.00620\n",
      "Epoch: 600, loss: 0.00609\n",
      "Epoch: 700, loss: 0.00602\n",
      "Epoch: 800, loss: 0.00599\n",
      "Epoch: 900, loss: 0.00595\n",
      "Epoch: 1000, loss: 0.00593\n",
      "Epoch: 1100, loss: 0.00592\n",
      "Epoch: 1200, loss: 0.00590\n",
      "Epoch: 1300, loss: 0.00596\n",
      "Epoch: 1400, loss: 0.00588\n",
      "Epoch: 1500, loss: 0.00587\n",
      "Epoch: 1600, loss: 0.00586\n",
      "Epoch: 1700, loss: 0.00584\n",
      "Epoch: 1800, loss: 0.00583\n",
      "Epoch: 1900, loss: 0.00584\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 2000\n",
    "learning_rate = 0.01\n",
    "\n",
    "ann = ANN()\n",
    "print(ann)\n",
    "criterion = PinballLoss(quantile=0.95)\n",
    "optimizer = torch.optim.Adam(ann.parameters(), lr=learning_rate)\n",
    "for epoch in range(num_epochs):\n",
    "    outputs = ann(trainX)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, trainY)\n",
    "    loss.backward()\n",
    "    \n",
    "    optimizer.step()\n",
    "    if epoch % 100 == 0:\n",
    "      print(\"Epoch: %d, loss: %1.5f\" % (epoch, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3500, 1])\n",
      "(3500, 1)\n",
      "(11495, 1)\n"
     ]
    }
   ],
   "source": [
    "ann.eval()\n",
    "test_predict = ann(testX)\n",
    "print(test_predict.shape)\n",
    "\n",
    "data_predict = test_predict.data.numpy()\n",
    "dataY_plot = dataY.data.numpy()\n",
    "print(data_predict.shape)\n",
    "print(dataY_plot.shape)\n",
    "\n",
    "d_p = np.concatenate((data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict),axis=1)\n",
    "dY_p = np.concatenate((dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot),axis=1)\n",
    "data_predict = sc.inverse_transform(d_p)\n",
    "dataY_plot = sc.inverse_transform(dY_p)\n",
    "\n",
    "dataY_plot = dataY_plot[:,0]\n",
    "data_predict = data_predict[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_bound = data_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANN(\n",
      "  (fc1): Linear(in_features=8, out_features=20, bias=True)\n",
      "  (fc2): Linear(in_features=20, out_features=1, bias=True)\n",
      ")\n",
      "Epoch: 0, loss: 0.18453\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1628: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.\n",
      "  warnings.warn(\"nn.functional.tanh is deprecated. Use torch.tanh instead.\")\n",
      "/opt/anaconda3/lib/python3.7/site-packages/torch/nn/functional.py:1639: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
      "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 100, loss: 0.01575\n",
      "Epoch: 200, loss: 0.01555\n",
      "Epoch: 300, loss: 0.01497\n",
      "Epoch: 400, loss: 0.00757\n",
      "Epoch: 500, loss: 0.00715\n",
      "Epoch: 600, loss: 0.00687\n",
      "Epoch: 700, loss: 0.00677\n",
      "Epoch: 800, loss: 0.00671\n",
      "Epoch: 900, loss: 0.00665\n",
      "Epoch: 1000, loss: 0.00660\n",
      "Epoch: 1100, loss: 0.00657\n",
      "Epoch: 1200, loss: 0.00654\n",
      "Epoch: 1300, loss: 0.00652\n",
      "Epoch: 1400, loss: 0.00651\n",
      "Epoch: 1500, loss: 0.00649\n",
      "Epoch: 1600, loss: 0.00647\n",
      "Epoch: 1700, loss: 0.00645\n",
      "Epoch: 1800, loss: 0.00644\n",
      "Epoch: 1900, loss: 0.00643\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 2000\n",
    "learning_rate = 0.01\n",
    "\n",
    "ann = ANN()\n",
    "print(ann)\n",
    "criterion = PinballLoss(quantile=0.05)\n",
    "optimizer = torch.optim.Adam(ann.parameters(), lr=learning_rate)\n",
    "for epoch in range(num_epochs):\n",
    "    outputs = ann(trainX)\n",
    "    optimizer.zero_grad()\n",
    "    loss = criterion(outputs, trainY)\n",
    "    loss.backward()\n",
    "    \n",
    "    optimizer.step()\n",
    "    if epoch % 100 == 0:\n",
    "      print(\"Epoch: %d, loss: %1.5f\" % (epoch, loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3500, 1])\n",
      "(3500, 1)\n",
      "(11495, 1)\n"
     ]
    }
   ],
   "source": [
    "ann.eval()\n",
    "test_predict = ann(testX)\n",
    "print(test_predict.shape)\n",
    "\n",
    "data_predict = test_predict.data.numpy()\n",
    "dataY_plot = dataY.data.numpy()\n",
    "print(data_predict.shape)\n",
    "print(dataY_plot.shape)\n",
    "\n",
    "d_p = np.concatenate((data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict,data_predict),axis=1)\n",
    "dY_p = np.concatenate((dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot,dataY_plot),axis=1)\n",
    "data_predict = sc.inverse_transform(d_p)\n",
    "dataY_plot = sc.inverse_transform(dY_p)\n",
    "\n",
    "dataY_plot = dataY_plot[:,0]\n",
    "data_predict = data_predict[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_bound = data_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unconditional_coverage(PIs, data_test):\n",
    "    indicator = []\n",
    "    for i in range(len(PIs)):\n",
    "        if PIs[i][0] < data_test[i] < PIs[i][1]:\n",
    "            indicator.append(1)\n",
    "        else:\n",
    "            indicator.append(0)\n",
    "    uc = np.sum(indicator)/len(indicator)\n",
    "    return uc\n",
    "\n",
    "def winlers_score(PIs, data_test, tau):\n",
    "    data_test = np.array(data_test)\n",
    "    indicators = []\n",
    "    for i in range(len(PIs)):\n",
    "        if PIs[i][0] < data_test[i] < PIs[i][1]:\n",
    "            score = PIs[i][1]-PIs[i][0]\n",
    "            indicators.append(score)\n",
    "        elif data_test[i] < PIs[i][0]:\n",
    "            score = (PIs[i][1]-PIs[i][0]) + (2 / tau * (PIs[i][0]-data_test[i]))\n",
    "            indicators.append(score)\n",
    "        elif PIs[i][1] < data_test[i]:\n",
    "            score = (PIs[i][1]-PIs[i][0]) + (2 / tau * (data_test[i]-PIs[i][1]))\n",
    "            indicators.append(score)\n",
    "    ws = np.sum(np.array(indicators))/len(indicators)\n",
    "    return ws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8871428571428571\n",
      "141.93797275652204\n"
     ]
    }
   ],
   "source": [
    "y_upper = np.array(upper_bound)\n",
    "y_lower = np.array(lower_bound)\n",
    "\n",
    "PIs_qrnn = []\n",
    "for i in range(len(y_upper)):\n",
    "    PIs_qrnn.append([y_lower[i], y_upper[i]])\n",
    "test_data = dataY_plot[7995:]\n",
    "len(test_data)\n",
    "uc_qrnn = unconditional_coverage(PIs_qrnn, test_data)\n",
    "print(uc_qrnn)\n",
    "ws_qrnn= winlers_score(PIs_qrnn, test_data, tau=0.10)\n",
    "print(ws_qrnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(PIs_qrnn).to_csv(\"PIs/PIs_qrnn.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "PIs_qrnn_xs = []\n",
    "PIs_qrnn_ys = []\n",
    "for i in range(len(PIs_qrnn)):\n",
    "    PIs_qrnn_xs.append(PIs_qrnn[i][0])\n",
    "    PIs_qrnn_ys.append(PIs_qrnn[i][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = np.arange(100)\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.fill_between(time, PIs_qrnn_xs[:100], PIs_qrnn_ys[:100], color=\"pink\")\n",
    "plt.plot(test_data[:100], marker=\"o\", color=\"black\")\n",
    "plt.ylabel(\"Traffic Flow\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
  },
  "kernelspec": {
   "display_name": "Python 3.7.9",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}