19_recurrent_neural_nets/04_multivariate_timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multivariate Time Series Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far, we have limited our modeling efforts to single time series. RNNs are naturally well suited to multivariate time series and represent a non-linear alternative to the Vector Autoregressive (VAR) models we covered in Chapter 8, Time Series Models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports & Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:00.929646Z",
     "start_time": "2021-02-23T06:48:00.927063Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:03.488363Z",
     "start_time": "2021-02-23T06:48:01.087274Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, LSTM\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:03.493600Z",
     "start_time": "2021-02-23T06:48:03.489755Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU\n"
     ]
    }
   ],
   "source": [
    "gpu_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "if gpu_devices:\n",
    "    print('Using GPU')\n",
    "    tf.config.experimental.set_memory_growth(gpu_devices[0], True)\n",
    "else:\n",
    "    print('Using CPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:03.503989Z",
     "start_time": "2021-02-23T06:48:03.498066Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:03.513670Z",
     "start_time": "2021-02-23T06:48:03.506274Z"
    }
   },
   "outputs": [],
   "source": [
    "results_path = Path('results', 'multivariate_time_series')\n",
    "if not results_path.exists():\n",
    "    results_path.mkdir(parents=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison, we illustrate the application of RNNs to modeling and forecasting several time series using the same dataset we used for the VAR example, monthly data on consumer sentiment, and industrial production from the Federal Reserve's FRED service in Chapter 8, Time Series Models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:06.127210Z",
     "start_time": "2021-02-23T06:48:05.578358Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 480 entries, 1980-01-01 to 2019-12-01\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   sentiment  480 non-null    float64\n",
      " 1   ip         480 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 11.2 KB\n"
     ]
    }
   ],
   "source": [
    "df = web.DataReader(['UMCSENT', 'IPGMFN'], 'fred', '1980', '2019-12').dropna()\n",
    "df.columns = ['sentiment', 'ip']\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:06.141239Z",
     "start_time": "2021-02-23T06:48:06.128947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>ip</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>67.0</td>\n",
       "      <td>46.8770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-02-01</th>\n",
       "      <td>66.9</td>\n",
       "      <td>47.9757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-03-01</th>\n",
       "      <td>56.5</td>\n",
       "      <td>48.4793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-04-01</th>\n",
       "      <td>52.7</td>\n",
       "      <td>47.0662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-05-01</th>\n",
       "      <td>51.7</td>\n",
       "      <td>45.6995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sentiment       ip\n",
       "DATE                          \n",
       "1980-01-01       67.0  46.8770\n",
       "1980-02-01       66.9  47.9757\n",
       "1980-03-01       56.5  48.4793\n",
       "1980-04-01       52.7  47.0662\n",
       "1980-05-01       51.7  45.6995"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stationarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We apply the same transformation—annual difference for both series, prior log-transform for industrial production—to achieve stationarity that we used in Chapter 8 on Time Series Models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:06.593041Z",
     "start_time": "2021-02-23T06:48:06.585035Z"
    }
   },
   "outputs": [],
   "source": [
    "df_transformed = (pd.DataFrame({'ip': np.log(df.ip).diff(12),\n",
    "                                'sentiment': df.sentiment.diff(12)})\n",
    "                  .dropna())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we scale the transformed data to the [0,1] interval:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:09.268098Z",
     "start_time": "2021-02-23T06:48:09.252298Z"
    }
   },
   "outputs": [],
   "source": [
    "df_transformed = df_transformed.apply(minmax_scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot original and transformed series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:11.249754Z",
     "start_time": "2021-02-23T06:48:09.554213Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, figsize=(14,4))\n",
    "columns={'ip': 'Industrial Production', 'sentiment': 'Sentiment'}\n",
    "df.rename(columns=columns).plot(ax=axes[0], title='Original Series')\n",
    "df_transformed.rename(columns=columns).plot(ax=axes[1], title='Transformed Series')\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.savefig(results_path / 'multi_rnn', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshape data into RNN format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can reshape directly to get non-overlapping series, i.e., one sample for each year (works only if the number of samples is divisible by window size):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:13.144650Z",
     "start_time": "2021-02-23T06:48:13.141342Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 12, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values.reshape(-1, 12, 2).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we want rolling, not non-overlapping lagged values. The create_multivariate_rnn_data function transforms a dataset of several time series into the shape required by the Keras RNN layers, namely `n_samples` x `window_size` x `n_series`, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:13.455841Z",
     "start_time": "2021-02-23T06:48:13.452960Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_multivariate_rnn_data(data, window_size):\n",
    "    y = data[window_size:]\n",
    "    n = data.shape[0]\n",
    "    X = np.stack([data[i: j] \n",
    "                  for i, j in enumerate(range(window_size, n))], axis=0)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use window_size of 24 months and obtain the desired inputs for our RNN model, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:13.761579Z",
     "start_time": "2021-02-23T06:48:13.759076Z"
    }
   },
   "outputs": [],
   "source": [
    "window_size = 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:13.963203Z",
     "start_time": "2021-02-23T06:48:13.915725Z"
    }
   },
   "outputs": [],
   "source": [
    "X, y = create_multivariate_rnn_data(df_transformed, window_size=window_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:14.063646Z",
     "start_time": "2021-02-23T06:48:14.059953Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((450, 18, 2), (450, 2))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:14.219349Z",
     "start_time": "2021-02-23T06:48:14.211554Z"
    }
   },
   "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>ip</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>0.526669</td>\n",
       "      <td>0.576214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-02-01</th>\n",
       "      <td>0.513795</td>\n",
       "      <td>0.502513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-03-01</th>\n",
       "      <td>0.542863</td>\n",
       "      <td>0.670017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-04-01</th>\n",
       "      <td>0.613397</td>\n",
       "      <td>0.832496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-05-01</th>\n",
       "      <td>0.731775</td>\n",
       "      <td>0.914573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ip  sentiment\n",
       "DATE                           \n",
       "1981-01-01  0.526669   0.576214\n",
       "1981-02-01  0.513795   0.502513\n",
       "1981-03-01  0.542863   0.670017\n",
       "1981-04-01  0.613397   0.832496\n",
       "1981-05-01  0.731775   0.914573"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we split our data into a train and a test set, using the last 24 months to test the out-of-sample performance, as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:16.428668Z",
     "start_time": "2021-02-23T06:48:16.426564Z"
    }
   },
   "outputs": [],
   "source": [
    "test_size =24\n",
    "train_size = X.shape[0]-test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:16.625590Z",
     "start_time": "2021-02-23T06:48:16.616996Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, y_train = X[:train_size], y[:train_size]\n",
    "X_test, y_test = X[train_size:], y[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:16.774725Z",
     "start_time": "2021-02-23T06:48:16.771731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((426, 18, 2), (24, 18, 2))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a similar architecture with two stacked LSTM layers with 12 and 6 units, respectively, followed by a fully-connected layer with 10 units. The output layer has two units, one for each time series. We compile them using mean absolute loss and the recommended RMSProp optimizer, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:17.259646Z",
     "start_time": "2021-02-23T06:48:17.255449Z"
    }
   },
   "outputs": [],
   "source": [
    "K.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:17.549799Z",
     "start_time": "2021-02-23T06:48:17.547640Z"
    }
   },
   "outputs": [],
   "source": [
    "n_features = output_size = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:17.838826Z",
     "start_time": "2021-02-23T06:48:17.836719Z"
    }
   },
   "outputs": [],
   "source": [
    "lstm_units = 12\n",
    "dense_units = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:18.232554Z",
     "start_time": "2021-02-23T06:48:18.007729Z"
    }
   },
   "outputs": [],
   "source": [
    "rnn = Sequential([\n",
    "    LSTM(units=lstm_units,\n",
    "         dropout=.1,\n",
    "         recurrent_dropout=.1,\n",
    "         input_shape=(window_size, n_features), name='LSTM',\n",
    "         return_sequences=False),\n",
    "    Dense(dense_units, name='FC'),\n",
    "    Dense(output_size, name='Output')\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has 1,268 parameters, as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:19.880001Z",
     "start_time": "2021-02-23T06:48:19.875069Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "LSTM (LSTM)                  (None, 12)                720       \n",
      "_________________________________________________________________\n",
      "FC (Dense)                   (None, 6)                 78        \n",
      "_________________________________________________________________\n",
      "Output (Dense)               (None, 2)                 14        \n",
      "=================================================================\n",
      "Total params: 812\n",
      "Trainable params: 812\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "rnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:20.055041Z",
     "start_time": "2021-02-23T06:48:20.037412Z"
    }
   },
   "outputs": [],
   "source": [
    "rnn.compile(loss='mae', optimizer='RMSProp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We train for 50 epochs with a batch_size value of 20 using early stopping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:20.477367Z",
     "start_time": "2021-02-23T06:48:20.470808Z"
    }
   },
   "outputs": [],
   "source": [
    "lstm_path = (results_path / 'lstm.h5').as_posix()\n",
    "\n",
    "checkpointer = ModelCheckpoint(filepath=lstm_path,\n",
    "                               verbose=1,\n",
    "                               monitor='val_loss',\n",
    "                               mode='min',\n",
    "                               save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:20.622189Z",
     "start_time": "2021-02-23T06:48:20.617111Z"
    }
   },
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss', \n",
    "                              patience=10,\n",
    "                              restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:30.587987Z",
     "start_time": "2021-02-23T06:48:20.771561Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.2743\n",
      "Epoch 00001: val_loss improved from inf to 0.04285, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 1s 25ms/step - loss: 0.2536 - val_loss: 0.0429\n",
      "Epoch 2/100\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.1013\n",
      "Epoch 00002: val_loss improved from 0.04285 to 0.03912, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0991 - val_loss: 0.0391\n",
      "Epoch 3/100\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.0956\n",
      "Epoch 00003: val_loss did not improve from 0.03912\n",
      "22/22 [==============================] - 0s 12ms/step - loss: 0.0941 - val_loss: 0.0404\n",
      "Epoch 4/100\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.0965\n",
      "Epoch 00004: val_loss improved from 0.03912 to 0.03764, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.0945 - val_loss: 0.0376\n",
      "Epoch 5/100\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.0910\n",
      "Epoch 00005: val_loss did not improve from 0.03764\n",
      "22/22 [==============================] - 0s 12ms/step - loss: 0.0918 - val_loss: 0.0504\n",
      "Epoch 6/100\n",
      "21/22 [===========================>..] - ETA: 0s - loss: 0.0903\n",
      "Epoch 00006: val_loss improved from 0.03764 to 0.03714, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0898 - val_loss: 0.0371\n",
      "Epoch 7/100\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.0898\n",
      "Epoch 00007: val_loss did not improve from 0.03714\n",
      "22/22 [==============================] - 0s 12ms/step - loss: 0.0885 - val_loss: 0.0376\n",
      "Epoch 8/100\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.0908\n",
      "Epoch 00008: val_loss did not improve from 0.03714\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0884 - val_loss: 0.0491\n",
      "Epoch 9/100\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.0899\n",
      "Epoch 00009: val_loss did not improve from 0.03714\n",
      "22/22 [==============================] - 0s 12ms/step - loss: 0.0876 - val_loss: 0.0418\n",
      "Epoch 10/100\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.0906\n",
      "Epoch 00010: val_loss improved from 0.03714 to 0.03557, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0892 - val_loss: 0.0356\n",
      "Epoch 11/100\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.0916\n",
      "Epoch 00011: val_loss did not improve from 0.03557\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0894 - val_loss: 0.0463\n",
      "Epoch 12/100\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.0883\n",
      "Epoch 00012: val_loss did not improve from 0.03557\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0877 - val_loss: 0.0389\n",
      "Epoch 13/100\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.0882\n",
      "Epoch 00013: val_loss did not improve from 0.03557\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0873 - val_loss: 0.0451\n",
      "Epoch 14/100\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.0879\n",
      "Epoch 00014: val_loss improved from 0.03557 to 0.03552, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.0867 - val_loss: 0.0355\n",
      "Epoch 15/100\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.0854\n",
      "Epoch 00015: val_loss improved from 0.03552 to 0.03534, saving model to results/multivariate_time_series/lstm.h5\n",
      "22/22 [==============================] - 0s 12ms/step - loss: 0.0837 - val_loss: 0.0353\n",
      "Epoch 16/100\n",
      "19/22 [========================>.....] - ETA: 0s - loss: 0.0864\n",
      "Epoch 00016: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0841 - val_loss: 0.0412\n",
      "Epoch 17/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0837\n",
      "Epoch 00017: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.0837 - val_loss: 0.0356\n",
      "Epoch 18/100\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.0859\n",
      "Epoch 00018: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 15ms/step - loss: 0.0845 - val_loss: 0.0357\n",
      "Epoch 19/100\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.0845\n",
      "Epoch 00019: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.0832 - val_loss: 0.0376\n",
      "Epoch 20/100\n",
      "20/22 [==========================>...] - ETA: 0s - loss: 0.0837\n",
      "Epoch 00020: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0824 - val_loss: 0.0357\n",
      "Epoch 21/100\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.0839\n",
      "Epoch 00021: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.0825 - val_loss: 0.0379\n",
      "Epoch 22/100\n",
      "21/22 [===========================>..] - ETA: 0s - loss: 0.0827\n",
      "Epoch 00022: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.0822 - val_loss: 0.0359\n",
      "Epoch 23/100\n",
      "22/22 [==============================] - ETA: 0s - loss: 0.0818\n",
      "Epoch 00023: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0818 - val_loss: 0.0375\n",
      "Epoch 24/100\n",
      "21/22 [===========================>..] - ETA: 0s - loss: 0.0823\n",
      "Epoch 00024: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 15ms/step - loss: 0.0820 - val_loss: 0.0359\n",
      "Epoch 25/100\n",
      "18/22 [=======================>......] - ETA: 0s - loss: 0.0823\n",
      "Epoch 00025: val_loss did not improve from 0.03534\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.0810 - val_loss: 0.0471\n"
     ]
    }
   ],
   "source": [
    "result = rnn.fit(X_train,\n",
    "                 y_train,\n",
    "                 epochs=100,\n",
    "                 batch_size=20,\n",
    "                 shuffle=False,\n",
    "                 validation_data=(X_test, y_test),\n",
    "                 callbacks=[early_stopping, checkpointer],\n",
    "                 verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training stops early after 22 epochs, yielding a test MAE of 1.71, which compares favorably to the test MAE for the VAR model of 1.91."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the two results are not fully comparable because the RNN model produces 24 one-step-ahead forecasts, whereas the VAR model uses its own predictions as input for its out-of-sample forecast. You may want to tweak the VAR setup to obtain comparable forecasts and compare their performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:31.068422Z",
     "start_time": "2021-02-23T06:48:30.875040Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(result.history).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:31.336932Z",
     "start_time": "2021-02-23T06:48:31.070644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 24 entries, 2018-01-01 to 2019-12-01\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   ip         24 non-null     float32\n",
      " 1   sentiment  24 non-null     float32\n",
      "dtypes: float32(2)\n",
      "memory usage: 384.0 bytes\n"
     ]
    }
   ],
   "source": [
    "y_pred = pd.DataFrame(rnn.predict(X_test), \n",
    "                      columns=y_test.columns, \n",
    "                      index=y_test.index)\n",
    "y_pred.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:34.325621Z",
     "start_time": "2021-02-23T06:48:34.318074Z"
    }
   },
   "outputs": [],
   "source": [
    "test_mae = mean_absolute_error(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:34.500788Z",
     "start_time": "2021-02-23T06:48:34.498065Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03533523602534612\n"
     ]
    }
   ],
   "source": [
    "print(test_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:34.658841Z",
     "start_time": "2021-02-23T06:48:34.654632Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-01', '2018-02-01', '2018-03-01', '2018-04-01',\n",
       "               '2018-05-01', '2018-06-01', '2018-07-01', '2018-08-01',\n",
       "               '2018-09-01', '2018-10-01', '2018-11-01', '2018-12-01',\n",
       "               '2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',\n",
       "               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',\n",
       "               '2019-09-01', '2019-10-01', '2019-11-01', '2019-12-01'],\n",
       "              dtype='datetime64[ns]', name='DATE', freq=None)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-23T06:48:37.198929Z",
     "start_time": "2021-02-23T06:48:35.206772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=(17, 4))\n",
    "pd.DataFrame(result.history).rename(columns={'loss': 'Training',\n",
    "                                              'val_loss': 'Validation'}).plot(ax=axes[0], title='Train & Validation Error')\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('MAE')\n",
    "col_dict = {'ip': 'Industrial Production', 'sentiment': 'Sentiment'}\n",
    "\n",
    "for i, col in enumerate(y_test.columns, 1):\n",
    "    y_train.loc['2010':, col].plot(ax=axes[i], label='training', title=col_dict[col])\n",
    "    y_test[col].plot(ax=axes[i], label='out-of-sample')\n",
    "    y_pred[col].plot(ax=axes[i], label='prediction')\n",
    "    axes[i].set_xlabel('')\n",
    "\n",
    "axes[1].set_ylim(.5, .9)\n",
    "axes[1].fill_between(x=y_test.index, y1=0.5, y2=0.9, color='grey', alpha=.5)\n",
    "\n",
    "axes[2].set_ylim(.3, .9)\n",
    "axes[2].fill_between(x=y_test.index, y1=0.3, y2=0.9, color='grey', alpha=.5)\n",
    "\n",
    "plt.legend()\n",
    "fig.suptitle('Multivariate RNN - Results | Test MAE = {:.4f}'.format(test_mae), fontsize=14)\n",
    "sns.despine()\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(top=.85)\n",
    "fig.savefig(results_path / 'multivariate_results', dpi=300);"
   ]
  }
 ],
 "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": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}