From f5f419906c81047d35bee58b1c059d061d45a362 Mon Sep 17 00:00:00 2001
From: pechyony <pechyony@outlook.com>
Date: Wed, 12 Jun 2019 11:52:32 -0400
Subject: [PATCH 1/2] first version of ES-RNN

---
 4_ES_RNN.ipynb | 1155 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1155 insertions(+)
 create mode 100644 4_ES_RNN.ipynb

diff --git a/4_ES_RNN.ipynb b/4_ES_RNN.ipynb
new file mode 100644
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--- /dev/null
+++ b/4_ES_RNN.ipynb
@@ -0,0 +1,1155 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multi step model (vector output approach)\n",
+    "\n",
+    "In this notebook, we demonstrate how to:\n",
+    "- prepare time series data for training a RNN forecasting model\n",
+    "- get data in the required shape for the keras API\n",
+    "- implement a RNN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses recent values of temperature and load as the model input. The model will be trained to output a vector, the elements of which are ordered predictions for future time steps.\n",
+    "- enable early stopping to reduce the likelihood of model overfitting\n",
+    "- evaluate the model on a test dataset\n",
+    "\n",
+    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load.\n",
+    "\n",
+    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "from collections import UserDict, deque\n",
+    "from IPython.display import Image\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\n",
+    "\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data into Pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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>load</th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "      <td>32.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "      <td>32.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "      <td>30.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "      <td>31.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "      <td>32.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load  temp\n",
+       "2012-01-01 00:00:00 2,698.00 32.00\n",
+       "2012-01-01 01:00:00 2,558.00 32.67\n",
+       "2012-01-01 02:00:00 2,444.00 30.00\n",
+       "2012-01-01 03:00:00 2,402.00 31.00\n",
+       "2012-01-01 04:00:00 2,403.00 32.00"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('data/')\n",
+    "energy.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "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>load</th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31 19:00:00</th>\n",
+       "      <td>4,012.00</td>\n",
+       "      <td>18.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31 20:00:00</th>\n",
+       "      <td>3,856.00</td>\n",
+       "      <td>16.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31 21:00:00</th>\n",
+       "      <td>3,671.00</td>\n",
+       "      <td>17.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31 22:00:00</th>\n",
+       "      <td>3,499.00</td>\n",
+       "      <td>15.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31 23:00:00</th>\n",
+       "      <td>3,345.00</td>\n",
+       "      <td>15.33</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load  temp\n",
+       "2014-12-31 19:00:00 4,012.00 18.00\n",
+       "2014-12-31 20:00:00 3,856.00 16.67\n",
+       "2014-12-31 21:00:00 3,671.00 17.00\n",
+       "2014-12-31 22:00:00 3,499.00 15.33\n",
+       "2014-12-31 23:00:00 3,345.00 15.33"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "valid_start_dt = '2014-08-30 08:00:00'\n",
+    "test_start_dt = '2014-10-31 11:00:00'\n",
+    "test_end_dt = '2014-12-30 18:00:00'\n",
+    "\n",
+    "T = 6\n",
+    "HORIZON = 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[energy.index < valid_start_dt][['load']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler = MinMaxScaler()\n",
+    "scaler.fit(train[['load']])\n",
+    "train[['load']] = scaler.transform(train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the TimeSeriesTensor convenience class to:\n",
+    "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n",
+    "2. Discard any samples with missing values\n",
+    "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n",
+    "\n",
+    "The class takes the following parameters:\n",
+    "\n",
+    "- **dataset**: original time series\n",
+    "- **H**: the forecast horizon\n",
+    "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n",
+    "- **freq**: time series frequency\n",
+    "- **drop_incomplete**: (Boolean) whether to drop incomplete samples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tensor_structure = {'X':(range(-T+1, 1), ['load'])}\n",
+    "train_inputs = TimeSeriesTensor(train, 'load', HORIZON, tensor_structure)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>tensor</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">target</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">X</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>feature</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">y</th>\n",
+       "      <th colspan=\"6\" halign=\"left\">load</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>time step</th>\n",
+       "      <th>t+1</th>\n",
+       "      <th>t+2</th>\n",
+       "      <th>t+3</th>\n",
+       "      <th>t-5</th>\n",
+       "      <th>t-4</th>\n",
+       "      <th>t-3</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>2012-01-01 05:00:00</th>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>0.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.15</td>\n",
+       "      <td>0.18</td>\n",
+       "      <td>0.23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "tensor              target              X                         \n",
+       "feature                  y           load                         \n",
+       "time step              t+1  t+2  t+3  t-5  t-4  t-3  t-2  t-1    t\n",
+       "2012-01-01 05:00:00   0.18 0.23 0.29 0.22 0.18 0.14 0.13 0.13 0.15\n",
+       "2012-01-01 06:00:00   0.23 0.29 0.35 0.18 0.14 0.13 0.13 0.15 0.18\n",
+       "2012-01-01 07:00:00   0.29 0.35 0.37 0.14 0.13 0.13 0.15 0.18 0.23"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_inputs.dataframe.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(23328, 9)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_inputs.dataframe.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(23328, 6, 1)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_inputs['X'].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0.22],\n",
+       "        [0.18],\n",
+       "        [0.14],\n",
+       "        [0.13],\n",
+       "        [0.13],\n",
+       "        [0.15]],\n",
+       "\n",
+       "       [[0.18],\n",
+       "        [0.14],\n",
+       "        [0.13],\n",
+       "        [0.13],\n",
+       "        [0.15],\n",
+       "        [0.18]],\n",
+       "\n",
+       "       [[0.14],\n",
+       "        [0.13],\n",
+       "        [0.13],\n",
+       "        [0.15],\n",
+       "        [0.18],\n",
+       "        [0.23]]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_inputs['X'][:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.18, 0.23, 0.29],\n",
+       "       [0.23, 0.29, 0.35],\n",
+       "       [0.29, 0.35, 0.37]])"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_inputs['target'][:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Construct validation set (keeping T hours from the training set in order to construct initial features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
+    "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "valid[['load']] = scaler.transform(valid)\n",
+    "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Implement the RNN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will implement a RNN forecasting model with the following structure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image('./images/multi_step_vector_output.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.models import Model\n",
+    "from keras.layers import Input, GRU, Dense, Lambda\n",
+    "from keras.callbacks import EarlyStopping"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "LATENT_DIM = 5\n",
+    "BATCH_SIZE = 48\n",
+    "EPOCHS = 1000\n",
+    "m = 24"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras import backend as K\n",
+    "from keras.layers import Layer\n",
+    "\n",
+    "class ES(Layer):\n",
+    "\n",
+    "    def __init__(self, horizon, m, batch_size, time_steps, **kwargs):\n",
+    "        self.horizon = horizon\n",
+    "        self.m = m\n",
+    "        self.batch_size = batch_size\n",
+    "        self.time_steps = time_steps\n",
+    "        \n",
+    "        super(ES, self).__init__(**kwargs)\n",
+    "\n",
+    "    def build(self, input_shape):\n",
+    "        self.alpha = self.add_weight(name='alpha', shape=(1,),\n",
+    "                                     initializer='uniform', trainable=True)\n",
+    "        self.gamma = self.add_weight(name='gamma', shape=(1,),\n",
+    "                                     initializer='uniform', trainable=True)\n",
+    "        self.init_seasonality = self.add_weight(name='init_seasonality', shape=(self.m,),\n",
+    "                                                initializer='uniform', trainable=True)\n",
+    "        self.init_seasonality_list = [K.slice(self.init_seasonality,(i,),(1,)) for i in range(self.m)]\n",
+    "        self.seasonality_queue = deque(self.init_seasonality_list, self.m)\n",
+    "        self.level = K.zeros((1,))\n",
+    "        \n",
+    "        super(ES, self).build(input_shape)  \n",
+    "\n",
+    "    def call(self, x):\n",
+    "\n",
+    "        n_examples = K.int_shape(x)[0]\n",
+    "        if n_examples is None:\n",
+    "            n_examples = self.batch_size\n",
+    "        x1 = K.slice(x,(0,0,0),(1,self.time_steps,1))\n",
+    "        x1 = K.reshape(x1,(self.time_steps,))\n",
+    "        x2 = K.slice(x,(1,self.time_steps-1,0),(n_examples-1,1,1))\n",
+    "        x2 = K.reshape(x2,(n_examples-1,))\n",
+    "        ts = K.concatenate([x1,x2])\n",
+    "        \n",
+    "        x_norm = []  # normalized values of time-series\n",
+    "        ls = []      # coeffients for denormalization of forecasts\n",
+    "        \n",
+    "        l_t_minus_1 = self.level\n",
+    "        \n",
+    "        for i in range(n_examples+self.time_steps-1):\n",
+    "        \n",
+    "            # compute l_t\n",
+    "            y_t = ts[i]\n",
+    "            s_t = self.seasonality_queue.popleft()\n",
+    "            l_t = self.alpha * y_t / s_t + (1 - self.alpha) * l_t_minus_1\n",
+    "            \n",
+    "            # compute s_{t+m}\n",
+    "            s_t_plus_m = self.gamma * y_t / l_t + (1 - self.gamma) * s_t\n",
+    "            \n",
+    "            self.seasonality_queue.append(s_t_plus_m)\n",
+    "            \n",
+    "            # normalize y_t\n",
+    "            x_norm.append(y_t / (s_t * l_t))\n",
+    "\n",
+    "            l_t_minus_1 = l_t\n",
+    "\n",
+    "            if i >= self.time_steps-1:\n",
+    "                l = [l_t]*self.horizon\n",
+    "                l = K.concatenate(l)\n",
+    "                s = [self.seasonality_queue[i] for i in range(self.horizon)] # we assume here that horizon < m\n",
+    "                s = K.concatenate(s)\n",
+    "                ls_t = K.concatenate([K.expand_dims(l), K.expand_dims(s)])\n",
+    "                ls.append(K.expand_dims(ls_t,axis=0))  \n",
+    "       \n",
+    "        self.level = l_t\n",
+    "        x_norm = K.concatenate(x_norm)\n",
+    "\n",
+    "        # create x_out\n",
+    "        x_out = []\n",
+    "        for i in range(n_examples):\n",
+    "            norm_features = K.slice(x_norm,(i,),(self.time_steps,))\n",
+    "            norm_features = K.expand_dims(norm_features,axis=0)\n",
+    "            x_out.append(norm_features)\n",
+    "\n",
+    "        x_out = K.concatenate(x_out, axis=0)\n",
+    "        x_out = K.expand_dims(x_out)\n",
+    "\n",
+    "        # create tensor of denormalization coefficients \n",
+    "        denorm_coeff = K.concatenate(ls, axis=0)\n",
+    "        return [x_out, denorm_coeff]\n",
+    "\n",
+    "    def compute_output_shape(self, input_shape):\n",
+    "        return [(input_shape[0], input_shape[1], input_shape[2]), (input_shape[0], self.horizon, 2)]\n",
+    "    \n",
+    "class Denormalization(Layer):\n",
+    "    \n",
+    "    def __init__(self, **kwargs):\n",
+    "        super(Denormalization, self).__init__(**kwargs)\n",
+    "\n",
+    "    def build(self, input_shape):\n",
+    "        super(Denormalization, self).build(input_shape)  \n",
+    "\n",
+    "    def call(self, x):\n",
+    "        return x[0] * x[1][:,:,0] * x[1][:,:,1]\n",
+    "\n",
+    "    def compute_output_shape(self, input_shape):\n",
+    "        return input_shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_input = Input(shape=(None, 1))\n",
+    "[normalized_input, denormalization_coeff] = ES(HORIZON, m, BATCH_SIZE, T)(model_input)\n",
+    "gru_out = GRU(LATENT_DIM)(normalized_input)\n",
+    "model_output_normalized = Dense(HORIZON)(gru_out)\n",
+    "model_output = Denormalization()([model_output_normalized, denormalization_coeff])\n",
+    "model = Model(inputs=model_input, outputs=model_output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer='RMSprop', loss='mse')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_2 (InputLayer)            (None, None, 1)      0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "es_2 (ES)                       [(None, None, 1), (N 26          input_2[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "gru_2 (GRU)                     (None, 5)            105         es_2[0][0]                       \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_2 (Dense)                 (None, 3)            18          gru_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "denormalization_2 (Denormalizat (None, 3)            0           dense_2[0][0]                    \n",
+      "                                                                 es_2[0][1]                       \n",
+      "==================================================================================================\n",
+      "Total params: 149\n",
+      "Trainable params: 149\n",
+      "Non-trainable params: 0\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running fit_loop\n",
+      "Train on 23328 samples, validate on 1488 samples\n",
+      "Epoch 1/1000\n",
+      "23328/23328 [==============================] - 11s 467us/step - loss: 0.2172 - val_loss: 0.1665\n",
+      "Epoch 2/1000\n",
+      "23328/23328 [==============================] - 4s 167us/step - loss: 0.2023 - val_loss: 1.1562\n",
+      "Epoch 3/1000\n",
+      "23328/23328 [==============================] - 5s 204us/step - loss: 0.5497 - val_loss: 0.1464\n",
+      "Epoch 4/1000\n",
+      "23328/23328 [==============================] - 4s 185us/step - loss: 0.7730 - val_loss: 0.1916\n",
+      "Epoch 5/1000\n",
+      "23328/23328 [==============================] - 7s 279us/step - loss: 0.0935 - val_loss: 0.0583\n",
+      "Epoch 6/1000\n",
+      "23328/23328 [==============================] - 6s 254us/step - loss: 0.0376 - val_loss: 0.0457\n",
+      "Epoch 7/1000\n",
+      "23328/23328 [==============================] - 5s 221us/step - loss: 0.0163 - val_loss: 0.0188\n",
+      "Epoch 8/1000\n",
+      "23328/23328 [==============================] - 5s 211us/step - loss: 0.0020 - val_loss: 0.0154\n",
+      "Epoch 9/1000\n",
+      "23328/23328 [==============================] - 6s 240us/step - loss: 0.0014 - val_loss: 0.0139\n",
+      "Epoch 10/1000\n",
+      "23328/23328 [==============================] - 6s 242us/step - loss: 0.0013 - val_loss: 0.0128\n",
+      "Epoch 11/1000\n",
+      "23328/23328 [==============================] - 6s 241us/step - loss: 0.0013 - val_loss: 0.0119\n",
+      "Epoch 12/1000\n",
+      "23328/23328 [==============================] - 6s 236us/step - loss: 0.0012 - val_loss: 0.0111\n",
+      "Epoch 13/1000\n",
+      "23328/23328 [==============================] - 5s 213us/step - loss: 0.0011 - val_loss: 0.0104\n",
+      "Epoch 14/1000\n",
+      "23328/23328 [==============================] - 5s 232us/step - loss: 0.0011 - val_loss: 0.0098\n",
+      "Epoch 15/1000\n",
+      "23328/23328 [==============================] - 6s 240us/step - loss: 0.0010 - val_loss: 0.0092\n",
+      "Epoch 16/1000\n",
+      "23328/23328 [==============================] - 6s 241us/step - loss: 9.9943e-04 - val_loss: 0.0087\n",
+      "Epoch 17/1000\n",
+      "23328/23328 [==============================] - 5s 220us/step - loss: 9.6632e-04 - val_loss: 0.0084\n",
+      "Epoch 18/1000\n",
+      "23328/23328 [==============================] - 5s 200us/step - loss: 9.4413e-04 - val_loss: 0.0082\n",
+      "Epoch 19/1000\n",
+      "23328/23328 [==============================] - 5s 196us/step - loss: 9.3430e-04 - val_loss: 0.0081\n",
+      "Epoch 20/1000\n",
+      "23328/23328 [==============================] - 4s 187us/step - loss: 9.2975e-04 - val_loss: 0.0079\n",
+      "Epoch 21/1000\n",
+      "23328/23328 [==============================] - 5s 203us/step - loss: 9.2745e-04 - val_loss: 0.0078\n",
+      "Epoch 22/1000\n",
+      "23328/23328 [==============================] - 4s 183us/step - loss: 9.2416e-04 - val_loss: 0.0077\n",
+      "Epoch 23/1000\n",
+      "23328/23328 [==============================] - 5s 193us/step - loss: 9.2364e-04 - val_loss: 0.0075\n",
+      "Epoch 24/1000\n",
+      "23328/23328 [==============================] - 4s 184us/step - loss: 9.2472e-04 - val_loss: 0.0072\n",
+      "Epoch 25/1000\n",
+      "23328/23328 [==============================] - 4s 173us/step - loss: 9.2271e-04 - val_loss: 0.0070\n",
+      "Epoch 26/1000\n",
+      "23328/23328 [==============================] - 4s 182us/step - loss: 9.2935e-04 - val_loss: 0.0069\n",
+      "Epoch 27/1000\n",
+      "23328/23328 [==============================] - 5s 202us/step - loss: 9.3293e-04 - val_loss: 0.0069\n",
+      "Epoch 28/1000\n",
+      "23328/23328 [==============================] - 4s 186us/step - loss: 9.3747e-04 - val_loss: 0.0068\n",
+      "Epoch 29/1000\n",
+      "23328/23328 [==============================] - 4s 169us/step - loss: 9.3997e-04 - val_loss: 0.0068\n",
+      "Epoch 30/1000\n",
+      "23328/23328 [==============================] - 4s 171us/step - loss: 9.4134e-04 - val_loss: 0.0067\n",
+      "Epoch 31/1000\n",
+      "23328/23328 [==============================] - 5s 194us/step - loss: 9.4315e-04 - val_loss: 0.0067\n",
+      "Epoch 32/1000\n",
+      "23328/23328 [==============================] - 4s 191us/step - loss: 9.4429e-04 - val_loss: 0.0067\n",
+      "Epoch 33/1000\n",
+      "23328/23328 [==============================] - 5s 208us/step - loss: 9.4499e-04 - val_loss: 0.0066\n",
+      "Epoch 34/1000\n",
+      "23328/23328 [==============================] - 5s 200us/step - loss: 9.4609e-04 - val_loss: 0.0066\n",
+      "Epoch 35/1000\n",
+      "23328/23328 [==============================] - 5s 207us/step - loss: 9.4652e-04 - val_loss: 0.0066\n",
+      "Epoch 36/1000\n",
+      "23328/23328 [==============================] - 4s 181us/step - loss: 9.4553e-04 - val_loss: 0.0067\n",
+      "Epoch 37/1000\n",
+      "23328/23328 [==============================] - 4s 173us/step - loss: 9.4340e-04 - val_loss: 0.0066\n",
+      "Epoch 38/1000\n",
+      "23328/23328 [==============================] - 4s 168us/step - loss: 9.4337e-04 - val_loss: 0.0066\n",
+      "Epoch 39/1000\n",
+      "23328/23328 [==============================] - 4s 171us/step - loss: 9.4323e-04 - val_loss: 0.0066\n",
+      "Epoch 40/1000\n",
+      "23328/23328 [==============================] - 4s 177us/step - loss: 9.4267e-04 - val_loss: 0.0066\n",
+      "Epoch 41/1000\n",
+      "23328/23328 [==============================] - 4s 178us/step - loss: 9.4105e-04 - val_loss: 0.0067\n",
+      "Epoch 42/1000\n",
+      "23328/23328 [==============================] - 4s 183us/step - loss: 9.3991e-04 - val_loss: 0.0067\n",
+      "Epoch 43/1000\n",
+      "23328/23328 [==============================] - 5s 206us/step - loss: 9.4007e-04 - val_loss: 0.0066\n",
+      "Epoch 44/1000\n",
+      "23328/23328 [==============================] - 5s 204us/step - loss: 9.4000e-04 - val_loss: 0.0066\n",
+      "Epoch 45/1000\n",
+      "23328/23328 [==============================] - 5s 209us/step - loss: 9.3964e-04 - val_loss: 0.0066\n",
+      "Epoch 46/1000\n",
+      "23328/23328 [==============================] - 4s 175us/step - loss: 9.3826e-04 - val_loss: 0.0066\n",
+      "Epoch 47/1000\n",
+      "23328/23328 [==============================] - 5s 219us/step - loss: 9.3876e-04 - val_loss: 0.0067\n",
+      "Epoch 48/1000\n",
+      "23328/23328 [==============================] - 4s 180us/step - loss: 9.3734e-04 - val_loss: 0.0067\n",
+      "Epoch 49/1000\n",
+      "23328/23328 [==============================] - 4s 192us/step - loss: 9.3785e-04 - val_loss: 0.0066\n",
+      "Epoch 50/1000\n",
+      "23328/23328 [==============================] - 4s 191us/step - loss: 9.3705e-04 - val_loss: 0.0066\n",
+      "Epoch 51/1000\n",
+      "23328/23328 [==============================] - 4s 192us/step - loss: 9.3795e-04 - val_loss: 0.0066\n",
+      "Epoch 52/1000\n",
+      "23328/23328 [==============================] - 6s 246us/step - loss: 9.3733e-04 - val_loss: 0.0067\n",
+      "Epoch 53/1000\n",
+      "23328/23328 [==============================] - 5s 231us/step - loss: 9.3450e-04 - val_loss: 0.0066\n",
+      "Epoch 54/1000\n",
+      "23328/23328 [==============================] - 6s 276us/step - loss: 9.3418e-04 - val_loss: 0.0066\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x20501547e80>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(train_inputs['X'],\n",
+    "          train_inputs['target'],\n",
+    "          batch_size=BATCH_SIZE,\n",
+    "          shuffle=False,\n",
+    "          epochs=EPOCHS,\n",
+    "          validation_data=(valid_inputs['X'], valid_inputs['target']),\n",
+    "          callbacks=[earlystop],\n",
+    "          verbose=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluate the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
+    "test = energy.copy()[test_start_dt:test_end_dt][['load']]\n",
+    "test[['load']] = scaler.transform(test)\n",
+    "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(test_inputs['X'], batch_size=BATCH_SIZE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.53, 0.65, 0.73],\n",
+       "       [0.5 , 0.58, 0.64],\n",
+       "       [0.48, 0.53, 0.57],\n",
+       "       ...,\n",
+       "       [0.53, 0.55, 0.68],\n",
+       "       [0.6 , 0.75, 0.92],\n",
+       "       [0.71, 0.86, 0.99]], dtype=float32)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-10-31 16:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,687.90</td>\n",
+       "      <td>3,437.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-10-31 17:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,613.06</td>\n",
+       "      <td>3,466.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-10-31 18:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,537.20</td>\n",
+       "      <td>3,374.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-10-31 19:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,318.07</td>\n",
+       "      <td>3,315.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-10-31 20:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,290.50</td>\n",
+       "      <td>3,142.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-10-31 16:00:00  t+1    3,687.90 3,437.00\n",
+       "1 2014-10-31 17:00:00  t+1    3,613.06 3,466.00\n",
+       "2 2014-10-31 18:00:00  t+1    3,537.20 3,374.00\n",
+       "3 2014-10-31 19:00:00  t+1    3,318.07 3,315.00\n",
+       "4 2014-10-31 20:00:00  t+1    3,290.50 3,142.00"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_df = create_evaluation_df(predictions, test_inputs, HORIZON, scaler)\n",
+    "eval_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute MAPE for each forecast horizon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "h\n",
+       "t+1   0.04\n",
+       "t+2   0.10\n",
+       "t+3   0.17\n",
+       "Name: APE, dtype: float64"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "eval_df.groupby('h')['APE'].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compute MAPE across all predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1025883440304847"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mape(eval_df['prediction'], eval_df['actual'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot actuals vs predictions at each horizon for first week of the test period. As is to be expected, predictions for one step ahead (*t+1*) are more accurate than those for 2 or 3 steps ahead"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "for t in range(1, HORIZON+1):\n",
+    "    plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "fig = plt.figure(figsize=(15, 8))\n",
+    "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax.plot(plot_df['timestamp'], plot_df['t+1'], color='blue', linewidth=4.0, alpha=0.75)\n",
+    "ax.plot(plot_df['timestamp'], plot_df['t+2'], color='blue', linewidth=3.0, alpha=0.5)\n",
+    "ax.plot(plot_df['timestamp'], plot_df['t+3'], color='blue', linewidth=2.0, alpha=0.25)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "ax.legend(loc='best')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 97c25948acfc0023986b160148c8ccdeeac0013c Mon Sep 17 00:00:00 2001
From: pechyony <pechyony@outlook.com>
Date: Wed, 12 Jun 2019 17:01:17 -0400
Subject: [PATCH 2/2] add image and improve comments

---
 4_ES_RNN.ipynb    |  33 +++++++++++++++++++++++++--------
 images/es_rnn.png | Bin 0 -> 34461 bytes
 2 files changed, 25 insertions(+), 8 deletions(-)
 create mode 100644 images/es_rnn.png

diff --git a/4_ES_RNN.ipynb b/4_ES_RNN.ipynb
index 6d83d60..b4921b7 100644
--- a/4_ES_RNN.ipynb
+++ b/4_ES_RNN.ipynb
@@ -4,12 +4,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Multi step model (vector output approach)\n",
+    "# ES-RNN model \n",
     "\n",
     "In this notebook, we demonstrate how to:\n",
     "- prepare time series data for training a RNN forecasting model\n",
     "- get data in the required shape for the keras API\n",
-    "- implement a RNN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses recent values of temperature and load as the model input. The model will be trained to output a vector, the elements of which are ordered predictions for future time steps.\n",
+    "- implement an ES-RNN model in keras to predict the next 3 steps ahead (time *t+1* to *t+3*) in the time series. This model uses recent values of load as the model input. The model will be trained to output a vector, the elements of which are ordered predictions for future time steps.\n",
     "- enable early stopping to reduce the likelihood of model overfitting\n",
     "- evaluate the model on a test dataset\n",
     "\n",
@@ -499,35 +499,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Implement the RNN"
+    "## Implement ES-RNN"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will implement a RNN forecasting model with the following structure:"
+    "We will implement ES-RNN forecasting model with the following structure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Image('./images/multi_step_vector_output.png')"
+    "Image('./images/es_rnn.png')"
    ]
   },
   {
@@ -553,6 +553,13 @@
     "m = 24"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the next cell we define custom \"Exponential Smoothing+Normalization\" and \"Denormalization\" layers"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 46,
@@ -562,6 +569,7 @@
     "from keras import backend as K\n",
     "from keras.layers import Layer\n",
     "\n",
+    "# Exponential Smoothing + Normalization\n",
     "class ES(Layer):\n",
     "\n",
     "    def __init__(self, horizon, m, batch_size, time_steps, **kwargs):\n",
@@ -572,6 +580,7 @@
     "        \n",
     "        super(ES, self).__init__(**kwargs)\n",
     "\n",
+    "    # initialization of the learned parameters of exponential smoothing\n",
     "    def build(self, input_shape):\n",
     "        self.alpha = self.add_weight(name='alpha', shape=(1,),\n",
     "                                     initializer='uniform', trainable=True)\n",
@@ -587,6 +596,7 @@
     "\n",
     "    def call(self, x):\n",
     "\n",
+    "        # extract time-series from feature vector\n",
     "        n_examples = K.int_shape(x)[0]\n",
     "        if n_examples is None:\n",
     "            n_examples = self.batch_size\n",
@@ -661,6 +671,13 @@
     "        return input_shape[0]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since Denormalization layer has inputs from two previous layers, we need to use functional API of Keras"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 47,
diff --git a/images/es_rnn.png b/images/es_rnn.png
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