Alpha_RNNs_DK2_electricity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "TDLTylUvNgsc",
    "outputId": "3b10b8af-3ede-4b3e-dc15-34e906d0c649"
   },
   "outputs": [],
   "source": [
    "# Alpha_RNNs-DK2_electricity\n",
    "# Author: Matthew Dixon\n",
    "# Version: 1.1 (27.2.2020)\n",
    "# License: MIT\n",
    "# Email: matthew.dixon@iit.edu\n",
    "# Notes: tested on Mac OS X with Python 3.6 and Tensorflow 1.3.0\n",
    "# Citation: Please cite the following reference if this notebook is used for research purposes:\n",
    "# M.F Dixon, Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks, preprint, 2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P9BywHfc63kb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# To support both python 2 and python 3\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy import random\n",
    "import os# Generate switching data set\n",
    "import random\n",
    "\n",
    "\n",
    "# Imports for alpha_rnns \n",
    "from IPython import display\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from keras import optimizers\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM, GRU, SimpleRNN\n",
    "from keras import optimizers\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.regularizers import l1,l2\n",
    "from keras import regularizers\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from keras import *\n",
    "import keras.models\n",
    "from alphaRNN import *\n",
    "from alphatRNN import *\n",
    "\n",
    "\t\n",
    "from numpy.random import seed\n",
    "from tensorflow import set_random_seed\n",
    "\n",
    "# To plot figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# Where to save the figures\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JA0onJt1FY38"
   },
   "outputs": [],
   "source": [
    "es = EarlyStopping(monitor='loss', mode='min', verbose=1, patience=50, min_delta=1e-6, restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "colab_type": "code",
    "id": "0wfj1k-XhsKp",
    "outputId": "5ecfdfc1-8eb3-4796-dee9-eefd8c6076dc"
   },
   "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>utc_timestamp</th>\n",
       "      <th>DK_2_load_actual_net_consumption_tso</th>\n",
       "      <th>DK_2_load_actual_tso</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-01-01T00:00:00Z</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>1499.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-01-01T01:00:00Z</td>\n",
       "      <td>1400.2</td>\n",
       "      <td>1439.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-01-01T02:00:00Z</td>\n",
       "      <td>1334.8</td>\n",
       "      <td>1372.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-01-01T03:00:00Z</td>\n",
       "      <td>1290.1</td>\n",
       "      <td>1324.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-01-01T04:00:00Z</td>\n",
       "      <td>1286.1</td>\n",
       "      <td>1314.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          utc_timestamp  DK_2_load_actual_net_consumption_tso  \\\n",
       "0  2008-01-01T00:00:00Z                                1468.0   \n",
       "1  2008-01-01T01:00:00Z                                1400.2   \n",
       "2  2008-01-01T02:00:00Z                                1334.8   \n",
       "3  2008-01-01T03:00:00Z                                1290.1   \n",
       "4  2008-01-01T04:00:00Z                                1286.1   \n",
       "\n",
       "   DK_2_load_actual_tso  \n",
       "0                1499.4  \n",
       "1                1439.4  \n",
       "2                1372.0  \n",
       "3                1324.9  \n",
       "4                1314.4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Load7.csv')\n",
    "df = pd.DataFrame(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "6FlzpkZ5hw58",
    "outputId": "c0d764ae-c972-40e1-9df1-5b018974b63f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "utc_timestamp\n",
       "2008-01-01T00:00:00Z    1468.0\n",
       "2008-01-01T01:00:00Z    1400.2\n",
       "2008-01-01T02:00:00Z    1334.8\n",
       "2008-01-01T03:00:00Z    1290.1\n",
       "2008-01-01T04:00:00Z    1286.1\n",
       "Name: DK_2_load_actual_net_consumption_tso, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni_data = df['DK_2_load_actual_net_consumption_tso']\n",
    "uni_date = df['utc_timestamp']\n",
    "uni_data.index = uni_date\n",
    "uni_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 503
    },
    "colab_type": "code",
    "id": "t7wGqBI6u0M6",
    "outputId": "1e57ab50-5144-43d8-8fa4-5995d7772331"
   },
   "outputs": [],
   "source": [
    "use_features = ['DK_2_load_actual_net_consumption_tso']\n",
    "target = 'DK_2_load_actual_net_consumption_tso'\n",
    "\n",
    "n_steps = 30 # number of lags to include in the model\n",
    "\n",
    "train_weight = 0.8 # train/test sploit\n",
    "\n",
    "split = int(len(df)*train_weight)\n",
    "df_train = df[use_features].iloc[:split]\n",
    "mu = np.float(df_train[use_features].mean())\n",
    "sigma = np.float(df_train[use_features].std())\n",
    "std_df_train = df_train[use_features].apply(lambda x: (x - mu) / sigma)\n",
    "\n",
    "df_train = std_df_train \n",
    "std_df_test = df[use_features].apply(lambda x: (x-mu)/sigma).iloc[split:]\n",
    "df_test = std_df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical diagnostics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Augmented Dickey-Fuller test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G3OuQC2DMzrb",
    "outputId": "f5329c8d-78be-47bc-b98d-d3f58ee1165d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-6.147178010841611 1.071996639935344e-06 1970 {'1%': -3.9633728248857185, '5%': -3.4127209637023594, '10%': -3.128363501600249}\n"
     ]
    }
   ],
   "source": [
    "adf, p, usedlag, nobs, cvs,aic=sm.tsa.stattools.adfuller(df['DK_2_load_actual_net_consumption_tso'][:2000], regression='ct', maxlag=30)\n",
    "print(adf,p, nobs, cvs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YjtI7xMOMzrh",
    "outputId": "e3df74f9-b487-4a81-b897-680ea85169ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.          0.95869224 -0.78468411  0.25597489 -0.24503089  0.07696352\n",
      "  0.06027825  0.04656948  0.02207589 -0.0342775  -0.12844243  0.01361154\n",
      "  0.21274128  0.0271157  -0.00395558  0.18832094  0.26271402  0.17903612\n",
      "  0.27500886  0.21321348  0.18391544  0.24179789  0.24270386 -0.02057275\n",
      " -0.44037336 -0.49887036  0.25897333 -0.06018779  0.0282418   0.00209932\n",
      "  0.04368898  0.0207136  -0.05294792 -0.05176967  0.00273881  0.0209483\n",
      "  0.02859691  0.01350573 -0.0375535  -0.0238018  -0.01615046 -0.01453972\n",
      "  0.03956624  0.03314696 -0.00702568  0.01773658  0.00520414  0.01548065\n",
      " -0.03919206 -0.10010371  0.14992878 -0.06932014  0.01395849 -0.01600043\n",
      "  0.03274036  0.01459499 -0.01020564  0.0037985   0.04368231  0.02375848\n",
      " -0.01113843  0.01358194  0.02174117  0.02947851  0.05393095  0.07272289\n",
      "  0.06230925  0.05656046  0.03770286  0.0480605   0.03369425 -0.0138217\n",
      " -0.11290996 -0.14944869  0.08581554 -0.02389512]\n"
     ]
    }
   ],
   "source": [
    "pacf=sm.tsa.stattools.pacf(df[use_features], nlags=75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sXrjCVApMzrm"
   },
   "outputs": [],
   "source": [
    "#n_steps=np.where(np.array(np.abs(pacf)>2.58/np.sqrt(len(df[use_features])))==False)[0][0] -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xi_RxgZuMzr9",
    "outputId": "609978d6-0b83-4b8f-db86-05238520b9d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c2e41ceb8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "plt.plot(pacf, label='PACF')\n",
    "plt.plot([2.58/np.sqrt(len(df[use_features]))]*75, label='Upper 99% confidence interval', color='black', linestyle='--')\n",
    "plt.plot([-2.58/np.sqrt(len(df[use_features]))]*75, label='Lower 99% confidence interval', color='black', linestyle='--')\n",
    "# show daily lags\n",
    "plt.plot(24*np.ones(len(np.arange(-0.85,1.1,0.05))), np.arange(-0.85,1.1,0.05), linestyle='--', color='grey')\n",
    "plt.plot(48*np.ones(len(np.arange(-0.85,1.1,0.05))), np.arange(-0.85,1.1,0.05), linestyle='--', color='grey')\n",
    "plt.plot(72*np.ones(len(np.arange(-0.85,1.1,0.05))), np.arange(-0.85,1.1,0.05), linestyle='--', color='grey')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data structure manipulation for RNNs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KTCv6NRBvBa4"
   },
   "outputs": [],
   "source": [
    "def get_lagged_features(value, n_steps,n_steps_ahead):\n",
    "    \"\"\"\n",
    "    value: feature value to be lagged\n",
    "    n_steps: number of lags, i.e. sequence length\n",
    "    n_steps_ahead: forecasting horizon\n",
    "    \"\"\"\n",
    "    lag_list = []\n",
    "    for lag in range(n_steps+n_steps_ahead-1, n_steps_ahead-1, -1):\n",
    "        lag_list.append(value.shift(lag))\n",
    "    return pd.concat(lag_list, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JE0sdbrNvM3R"
   },
   "outputs": [],
   "source": [
    "n_steps_ahead=10\n",
    "\n",
    "x_train_list = []\n",
    "for use_feature in use_features:\n",
    "    x_train_reg = get_lagged_features(df_train, n_steps, n_steps_ahead).dropna()\n",
    "    x_train_list.append(x_train_reg)\n",
    "x_train_reg = pd.concat(x_train_list, axis=1)\n",
    "\n",
    "col_ords = []\n",
    "for i in range(n_steps):\n",
    "    for j in range(len(use_features)):\n",
    "        col_ords.append(i + j * n_steps)\n",
    "\n",
    "x_train_reg = x_train_reg.iloc[:, col_ords]\n",
    "y_train_reg = df_train.loc[x_train_reg.index, [target]].values\n",
    "x_train_reg = np.reshape(x_train_reg.values, (x_train_reg.shape[0], np.int(x_train_reg.shape[1] / len(use_features)), len(use_features)))\n",
    "y_train_reg = np.reshape(y_train_reg, (y_train_reg.shape[0], 1, 1))\n",
    "\n",
    "x_test_list = []\n",
    "for use_feature in use_features:\n",
    "    x_test_reg = get_lagged_features(df_test, n_steps,n_steps_ahead).dropna()\n",
    "    x_test_list.append(x_test_reg)\n",
    "x_test_reg = pd.concat(x_test_list, axis=1)\n",
    "\n",
    "x_test_reg = x_test_reg.iloc[:, col_ords]\n",
    "y_test_reg = df_test.loc[x_test_reg.index, [target]].values\n",
    "x_test_reg = np.reshape(x_test_reg.values, (x_test_reg.shape[0], np.int(x_test_reg.shape[1]/len(use_features)), len(use_features)))\n",
    "\n",
    "y_test_reg = np.reshape(y_test_reg, (y_test_reg.shape[0], 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "MBwt67S4vQmu",
    "outputId": "4270bbe6-4aab-4452-e457-4539139f82ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(23960, 30, 1) (23960, 1, 1) (5961, 30, 1) (5961, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "print(x_train_reg.shape,y_train_reg.shape,x_test_reg.shape,y_test_reg.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AFhK-lgavTuB"
   },
   "outputs": [],
   "source": [
    "train_batch_size = y_train_reg.shape[0]\n",
    "test_batch_size = y_test_reg.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "COZs3KSovWsf",
    "outputId": "719ce6b8-529c-4f34-a19c-8da735f883b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23960\n"
     ]
    }
   ],
   "source": [
    "print(train_batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aHJkBQ3XDO0h"
   },
   "outputs": [],
   "source": [
    "x_train_reg = pd.concat(x_train_list, axis=1)\n",
    "x_train_reg = x_train_reg.iloc[:, col_ords]\n",
    "y_train_reg = df_train.loc[x_train_reg.index, [target]].values\n",
    "x_train_reg = np.reshape(x_train_reg.values, (x_train_reg.shape[0], np.int(x_train_reg.shape[1] / len(use_features)), len(use_features)))\n",
    "\n",
    "\n",
    "x_test_reg = pd.concat(x_test_list, axis=1)\n",
    "x_test_reg = x_test_reg.iloc[:, col_ords]\n",
    "y_test_reg = df_test.loc[x_test_reg.index, [target]].values\n",
    "x_test_reg = np.reshape(x_test_reg.values, (x_test_reg.shape[0], np.int(x_test_reg.shape[1]/len(use_features)), len(use_features)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keras Wrappers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hHNIcbd7e0p1"
   },
   "outputs": [],
   "source": [
    "def AlphatRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(AlphatRNN(n_units, activation='tanh', recurrent_activation='sigmoid', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def AlphaRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(AlphaRNN(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True))\n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def SimpleRNN_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(SimpleRNN(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True, stateful=False))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def GRU_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(GRU(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True))  \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model\n",
    "\n",
    "\n",
    "def LSTM_(n_units = 10, l1_reg=0, seed=0):\n",
    "  model = Sequential()\n",
    "  model.add(LSTM(n_units, activation='tanh', kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), recurrent_initializer=keras.initializers.orthogonal(seed), kernel_regularizer=l1(l1_reg), input_shape=(x_train_reg.shape[1], x_train_reg.shape[-1]), unroll=True)) \n",
    "  model.add(Dense(1, kernel_initializer=keras.initializers.glorot_uniform(seed), bias_initializer=keras.initializers.glorot_uniform(seed), kernel_regularizer=l1(l1_reg)))\n",
    "  model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_epoches=2000\n",
    "batch_size=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zzdyxHk1Ftz2"
   },
   "outputs": [],
   "source": [
    "params = {'rnn': {'model':'', 'function':SimpleRNN_, 'l1_reg':0.0, 'H':20, 'color': 'blue', 'label':'RNN'}, \n",
    "          'alpharnn': {'model':'', 'function':AlphaRNN_,'l1_reg':0.0, 'H':20,'color': 'green', 'label': '$\\\\alpha$-RNN' }, \n",
    "          'alphatrnn': {'model':'', 'function':AlphatRNN_,'l1_reg':0.0, 'H':20, 'color': 'cyan', 'label': '$\\\\alpha_t$-RNN'},\n",
    "          'gru': {'model':'', 'function':GRU_,'l1_reg':0.0, 'H':20, 'color': 'orange', 'label': 'GRU'},\n",
    "          'lstm': {'model':'', 'function':LSTM_,'l1_reg':0.0, 'H':20, 'color':'red', 'label': 'LSTM'}\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0MaRQVPEMzx9"
   },
   "outputs": [],
   "source": [
    "cross_val=False # Warning: Changing this to True will take several hours to run\n",
    "if cross_val:\n",
    "    n_units = [5,10,20]\n",
    "    l1_reg = [0, 0.001, 0.01, 0.1]\n",
    "    tscv = TimeSeriesSplit(n_splits = 5)\n",
    "    param_grid = dict(n_units=n_units,l1_reg=l1_reg)\n",
    "\n",
    "    for key in params.keys(): # params[key]['function']\n",
    "        model = KerasRegressor(build_fn=params[key]['function'], epochs=max_epoches, batch_size=batch_size, verbose=2)\n",
    "        grid = GridSearchCV(estimator=model,param_grid=param_grid, cv=tscv, n_jobs=1, verbose=2)\n",
    "        grid_result = grid.fit(x_train_reg,y_train_reg,callbacks=[es])\n",
    "        print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "        means = grid_result.cv_results_['mean_test_score']\n",
    "        stds = grid_result.cv_results_['std_test_score']\n",
    "        params_ = grid_result.cv_results_['params']\n",
    "        for mean, stdev, param_ in zip(means, stds, params_):\n",
    "            print(\"%f (%f) with %r\" % (mean, stdev, param_))\n",
    "\n",
    "    params[key]['H'] = grid_result.best_params_['n_units']\n",
    "    params[key]['l1_reg']= grid_result.best_params_['l1_reg']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train cross-validated model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "23960/23960 [==============================] - 1s 29us/step - loss: 2.2004\n",
      "Epoch 2/2000\n",
      "23960/23960 [==============================] - 0s 6us/step - loss: 1.7647\n",
      "Epoch 3/2000\n",
      "23960/23960 [==============================] - 0s 6us/step - loss: 1.5687\n",
      "Epoch 4/2000\n",
      "23960/23960 [==============================] - 0s 6us/step - loss: 1.4162\n",
      "Epoch 5/2000\n",
      "23960/23960 [==============================] - 0s 6us/step - loss: 1.2638\n",
      "Epoch 6/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 1.1322\n",
      "Epoch 7/2000\n",
      "23960/23960 [==============================] - 0s 6us/step - loss: 1.0575\n",
      "Epoch 8/2000\n",
      "23960/23960 [==============================] - 0s 6us/step - loss: 1.0218\n",
      "Epoch 9/2000\n",
      "23960/23960 [==============================] - 0s 6us/step - loss: 1.0025\n",
      "Epoch 10/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9918\n",
      "Epoch 11/2000\n",
      "23960/23960 [==============================] - 0s 6us/step - loss: 0.9857\n",
      "Epoch 12/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9821\n",
      "Epoch 13/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9795\n",
      "Epoch 14/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.9775\n",
      "Epoch 15/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9757\n",
      "Epoch 16/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.9740\n",
      "Epoch 17/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9723\n",
      "Epoch 18/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9706\n",
      "Epoch 19/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9688\n",
      "Epoch 20/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.9669\n",
      "Epoch 21/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9648\n",
      "Epoch 22/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9625\n",
      "Epoch 23/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9598\n",
      "Epoch 24/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.9567\n",
      "Epoch 25/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9532\n",
      "Epoch 26/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9489\n",
      "Epoch 27/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9438\n",
      "Epoch 28/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.9374\n",
      "Epoch 29/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.9286\n",
      "Epoch 30/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.9146\n",
      "Epoch 31/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.8860\n",
      "Epoch 32/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.8180\n",
      "Epoch 33/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.7328\n",
      "Epoch 34/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.6550\n",
      "Epoch 35/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.5972\n",
      "Epoch 36/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.5463\n",
      "Epoch 37/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.4958\n",
      "Epoch 38/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.4512\n",
      "Epoch 39/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.4234\n",
      "Epoch 40/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.4015\n",
      "Epoch 41/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.3851\n",
      "Epoch 42/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.3694\n",
      "Epoch 43/2000\n",
      "23960/23960 [==============================] - 0s 9us/step - loss: 0.3536\n",
      "Epoch 44/2000\n",
      "23960/23960 [==============================] - 0s 9us/step - loss: 0.3371\n",
      "Epoch 45/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.3198\n",
      "Epoch 46/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.3014\n",
      "Epoch 47/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2827\n",
      "Epoch 48/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2658\n",
      "Epoch 49/2000\n",
      "23960/23960 [==============================] - 0s 10us/step - loss: 0.2530\n",
      "Epoch 50/2000\n",
      "23960/23960 [==============================] - 0s 9us/step - loss: 0.2452\n",
      "Epoch 51/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2403\n",
      "Epoch 52/2000\n",
      "23960/23960 [==============================] - 0s 9us/step - loss: 0.2368\n",
      "Epoch 53/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2333\n",
      "Epoch 54/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2308\n",
      "Epoch 55/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2286\n",
      "Epoch 56/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2266\n",
      "Epoch 57/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2248\n",
      "Epoch 58/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2232\n",
      "Epoch 59/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2216\n",
      "Epoch 60/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2202\n",
      "Epoch 61/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2189\n",
      "Epoch 62/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2176\n",
      "Epoch 63/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2165\n",
      "Epoch 64/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2154\n",
      "Epoch 65/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2143\n",
      "Epoch 66/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2133\n",
      "Epoch 67/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2123\n",
      "Epoch 68/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2114\n",
      "Epoch 69/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2104\n",
      "Epoch 70/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2094\n",
      "Epoch 71/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2084\n",
      "Epoch 72/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2074\n",
      "Epoch 73/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2064\n",
      "Epoch 74/2000\n",
      "23960/23960 [==============================] - 0s 7us/step - loss: 0.2054\n",
      "Epoch 75/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2044\n",
      "Epoch 76/2000\n",
      "23960/23960 [==============================] - 0s 8us/step - loss: 0.2035\n",
      "Epoch 77/2000\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Restoring model weights from the end of the best epoch\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     "text": [
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     ]
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 72/2000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "text": [
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      "23960/23960 [==============================] - 1s 29us/step - loss: 0.0660\n",
      "Epoch 583/2000\n",
      "23960/23960 [==============================] - 1s 29us/step - loss: 0.0662\n",
      "Epoch 584/2000\n",
      "23960/23960 [==============================] - 1s 26us/step - loss: 0.0663\n",
      "Epoch 585/2000\n",
      "23960/23960 [==============================] - 1s 28us/step - loss: 0.0662\n",
      "Epoch 586/2000\n",
      "23960/23960 [==============================] - 1s 28us/step - loss: 0.0658\n",
      "Epoch 587/2000\n",
      "23960/23960 [==============================] - 1s 27us/step - loss: 0.0652\n",
      "Epoch 588/2000\n",
      "23960/23960 [==============================] - 1s 32us/step - loss: 0.0646\n",
      "Epoch 589/2000\n",
      "23960/23960 [==============================] - 1s 37us/step - loss: 0.0641\n",
      "Epoch 590/2000\n",
      "23960/23960 [==============================] - 1s 40us/step - loss: 0.0638\n",
      "Epoch 591/2000\n",
      "23960/23960 [==============================] - 1s 33us/step - loss: 0.0640\n",
      "Epoch 592/2000\n",
      "23960/23960 [==============================] - 1s 34us/step - loss: 0.0651\n",
      "Epoch 593/2000\n",
      "23960/23960 [==============================] - 1s 33us/step - loss: 0.0669\n",
      "Epoch 594/2000\n",
      "23960/23960 [==============================] - 1s 31us/step - loss: 0.0686\n",
      "Epoch 595/2000\n",
      "23960/23960 [==============================] - 1s 30us/step - loss: 0.0687\n",
      "Epoch 596/2000\n",
      "23960/23960 [==============================] - 1s 29us/step - loss: 0.0671\n",
      "Epoch 597/2000\n",
      "15000/23960 [=================>............] - ETA: 0s - loss: 0.0626"
     ]
    }
   ],
   "source": [
    "for key in params.keys():\n",
    "    set_random_seed(0)\n",
    "    model=params[key]['function'](params[key]['H'],params[key]['l1_reg'])\n",
    "    model.fit(x_train_reg,y_train_reg,epochs=max_epoches, batch_size=batch_size,callbacks=[es],shuffle=False)\n",
    "    params[key]['model']=model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,7))\n",
    "upper = 2000\n",
    "plt.plot(df_test.index[n_steps+n_steps_ahead-1:upper], df_test[use_feature][n_steps+n_steps_ahead-1:upper], color=\"black\", label=\"Observed\")\n",
    "\n",
    "for key in params.keys():\n",
    "  plt.plot(df_test.index[n_steps+n_steps_ahead-1:upper], params[key]['predict'][:upper-(n_steps+n_steps_ahead-1), 0], color=params[key]['color'], label=params[key]['label'])\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.xlabel('Time', fontsize=20)\n",
    "plt.ylabel('Y', fontsize=20)\n",
    "plt.title('Observed vs Model (Testing)', fontsize=16)\n",
    "\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot prediction error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(12,7))\n",
    "\n",
    "for key in params.keys():\n",
    "  plt.plot(df_test.index[n_steps+n_steps_ahead-1:upper], df_test[use_feature][n_steps+n_steps_ahead-1:upper]-params[key]['predict'][:upper-(n_steps+n_steps_ahead-1), 0], color=params[key]['color'], label=params[key]['label'] + \" (\" +  str(round(params[key]['MSE_test'],3)) +\")\")\n",
    "\n",
    "plt.legend(loc=\"best\", fontsize=12)\n",
    "plt.title('Observed vs Model Error (Training)', fontsize=16)\n",
    "plt.xlabel('Time', fontsize=20)\n",
    "plt.ylabel('Y-$\\hat{Y}$', fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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