This tutorial contains brief overview of statistical and machine learning methods for time series forecasting, experiments and comparative analysis of Long short-term memory (LSTM) based architectures for solving above mentioned problem. Single layer, two layer and bidirectional single layer LSTM cases are considered. Metro Interstate Traffic Volume Data Set from UCI Machine Learning Repository and PyTorch deep learning framework are used for analysis. ### Time series forecasting methods overview A time series is a set of observations, each one is being recorded at the specific time $$t$$. It can be weather observations, for example, a records of the temperature for a month, it can be observations of currency quotes during the day or any other process aggregated by time. **Time series forecasting** can be determened as the act of predicting the future by understanding the past. The model for forecasting can rely on one variable and this is a **univariate case** or when more than one variable taken into consideration it will be **multivariate case**. **Stochastic Linear Models:** - Autoregressive (AR); - Moving Average (MA); - Autoregressive Moving Average (ARMA); - Autoregressive Integrated Moving Average (ARIMA); - Seasonal ARIMA (SARIMA). For above family of models, the **stationarity condition** must be satisfied. Loosely speaking, a stochastic process is stationary, if its statistical properties do not change with time. **Stochastic Non-Linear Models:** - nonlinear autoregressive exogenous models (NARX); - autoregressive conditional heteroskedasticity (ARCH); - generalized autoregressive conditional heteroskedasticity (GARCH). **Machine Learning Models** - Hidden Markov Model; - Least-square SVM (LS-SVM); - Dynamic Least-square SVM (DLS-SVM); - Feed Forward Network (FNN); - Time Lagged Neural Network (TLNN); - Seasonal Artificial Neural Network (SANN); - Recurrent Neural Networks (RNN). ### Dataset We will use [Metro Interstate Traffic Volume Data Set](https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume) from UC Irvine Machine Learning Repository, which contains large number of datasets for various tasks in machine learning. We will investigate how weather and holiday features influence the metro traffic in US. Attribute Information: **holiday** — Categorical US National holidays plus regional holiday, Minnesota State;
**temp** — Numeric, average temp in kelvin;
**rain_1h** — Numeric, amount in mm of rain that occurred in the hour;
**snow_1h** — Numeric, amount in mm of snow that occurred in the hour;
**clouds_all** — Numeric, percentage of cloud cover;
**weather_main** — Categorical, short textual description of the current weather;
**weather_description** — Categorical, longer textual description of the current weather;
**date_time** — DateTime, hour of the data collected in local CST time;
**traffic_volume** — Numeric, hourly I-94 ATR 301 reported westbound traffic volume. Our target variable will be **traffic_volume**. Here we will consider multivariate multi-step prediction case with LSTM-based recurrent neural network architecture. ### Metro Traffic Prediction using LSTM-based recurrent neural network [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MariaZork/my-machine-learning-tutorials/blob/master/time-series-forecasting-part1-LSTM.ipynb) I used Google Colab Notebooks to calculate experiments. Here, for convenience, I mounted my Google Drive where I stored the files. {`from google.colab import drive drive.mount('/content/drive', force_remount=True)`} {`import os import copy import random import numpy as np import pandas as pd import matplotlib.pyplot as plt import torch import torch.nn as nn from torch.autograd import Variable import warnings warnings.filterwarnings('ignore')`} {`random.seed(0) np.random.seed(0) torch.manual_seed(0) torch.cuda.manual_seed(0) torch.backends.cudnn.deterministic = True`} {`data = pd.read_csv( '/content/drive/My Drive/time series prediction/Metro_Interstate_Traffic_Volume.csv', parse_dates=True )`} #### Exploratory Data Analysis (EDA) and Scaling {`data.head()`}

	holiday	temp	clouds_all	weather_main	weather_description	date_time	traffic_volume
0	None	288.28	40	Clouds	scattered clouds	2012-10-02 09:00:00	5545
1	None	289.36	75	Clouds	broken clouds	2012-10-02 10:00:00	4516
2	None	289.58	90	Clouds	overcast clouds	2012-10-02 11:00:00	4767
3	None	290.13	90	Clouds	overcast clouds	2012-10-02 12:00:00	5026
4	None	291.14	75	Clouds	broken clouds	2012-10-02 13:00:00	4918

`} /> **Сategorical** features: holiday, weather_main, weather_description.
**Continious** features: temp, rain_1h, show_1h, clouds_all.
**Target variable**: traffic_volume <class 'pandas.core.frame.DataFrame'> RangeIndex: 48204 entries, 0 to 48203 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 holiday 48204 non-null object 1 temp 48204 non-null float64 2 rain_1h 48204 non-null float64 3 snow_1h 48204 non-null float64 4 clouds_all 48204 non-null int64 5 weather_main 48204 non-null object 6 weather_description 48204 non-null object 7 date_time 48204 non-null object 8 traffic_volume 48204 non-null int64 dtypes: float64(3), int64(2), object(4) memory usage: 3.3+ MB`} > {`data.info()`} Checking for Nan values holiday 0 temp 0 rain_1h 0 snow_1h 0 clouds_all 0 weather_main 0 weather_description 0 date_time 0 traffic_volume 0`} > {`for column in data.columns: print(column, sum(data[column].isna()))`} Here I take into consideration two categorical variables, such as *holiday* and *weather_main* and then one-hot encode them. {`data = pd.get_dummies(data, columns = ['holiday', 'weather_main'], drop_first=True)`}

	temp	clouds_all	weather_description	date_time	traffic_volume	holiday_None	weather_main_Clouds
0	288.28	40	scattered clouds	2012-10-02 09:00:00	5545	1	1
1	289.36	75	broken clouds	2012-10-02 10:00:00	4516	1	1
2	289.58	90	overcast clouds	2012-10-02 11:00:00	4767	1	1
3	290.13	90	overcast clouds	2012-10-02 12:00:00	5026	1	1
4	291.14	75	broken clouds	2012-10-02 13:00:00	4918	1	1

`} > {`data.head()`} {`data.shape`} Here we can see outliers in *temp* variable, lets filter outliers with Interquartile range (IQR) method Temperature plot

}> {`data['temp'].plot() plt.show() data['traffic_volume'].plot() plt.show()`} {`temp 19.646 rain_1h 0.000 snow_1h 0.000 clouds_all 89.000 traffic_volume 3740.000 holiday_Columbus Day 0.000 holiday_Independence Day 0.000 holiday_Labor Day 0.000 holiday_Martin Luther King Jr Day 0.000 holiday_Memorial Day 0.000 holiday_New Years Day 0.000 holiday_None 0.000 holiday_State Fair 0.000 holiday_Thanksgiving Day 0.000 holiday_Veterans Day 0.000 holiday_Washingtons Birthday 0.000 weather_main_Clouds 1.000 weather_main_Drizzle 0.000 weather_main_Fog 0.000 weather_main_Haze 0.000 weather_main_Mist 0.000 weather_main_Rain 0.000 weather_main_Smoke 0.000 weather_main_Snow 0.000 weather_main_Squall 0.000 weather_main_Thunderstorm 0.000 dtype: float64`}} > {`Q1 = data.quantile(0.25) Q3 = data.quantile(0.75) IQR = Q3 - Q1 print(IQR)`} {`data = data[~((data['temp'] < (Q1['temp'] - 1.5 * IQR['temp'])) |(data['temp'] > (Q3['temp'] + 1.5 * IQR['temp'])))]`} Temperature plot after outlier removal

Traffic volume plot after outlier removal

} > {`data['temp'].plot() plt.show() data['traffic_volume'].plot() plt.show()`} {`data.drop(columns=['rain_1h', 'snow_1h', 'weather_description', 'date_time'], inplace=True)`} ### Normalizing the features {`features_to_norm = ['temp', 'clouds_all', 'traffic_volume']`} {`TRAIN_SPLIT = 40000 STEP = 6 past_history = 720 future_target = 32 target_index = 2 tmp = data[features_to_norm].values data_mean = tmp[:TRAIN_SPLIT].mean(axis=0) data_std = tmp[:TRAIN_SPLIT].std(axis=0)`} {`data[features_to_norm] = (data[features_to_norm]-data_mean)/data_std`}

Index	temp	clouds_all	traffic_volume	holiday_Columbus Day	...
0	0.581321	-0.261692	1.145383	0	...
1	0.668789	0.638749	0.628729	0	...
...	...	...	...	...	...
48203	0.082432	1.024652	-1.159730	0	...

48194 rows × 24 columns

} > {`data`} ### Preparing training dataset and Visualizations Here we will consider multiple future points prediction case given a past history. {`def multivariate_data(dataset, target, start_index, end_index, history_size, target_size, step, single_step=False): data = [] labels = [] start_index = start_index + history_size if end_index is None: end_index = len(dataset) - target_size for i in range(start_index, end_index): indices = range(i-history_size, i, step) data.append(dataset[indices]) if single_step: labels.append(target[i+target_size]) else: labels.append(target[i:i+target_size]) return np.array(data), np.array(labels)`} {`trainX, trainY = multivariate_data(data.values, data.iloc[:, target_index].values, 0, TRAIN_SPLIT, past_history, future_target, STEP) valX, valY = multivariate_data(data.values, data.iloc[:, target_index].values, TRAIN_SPLIT, None, past_history, future_target, STEP)`} {`testX, testY = valX[:len(valX)//4], valY[:len(valY)//4] valX, valY = valX[len(valX)//4:], valY[len(valY)//4:]`} {`Train input features shape : (39280, 120, 24) Train output shape : (39280, 32) Validation input features shape : (5582, 120, 24) Validation output shape : (5582, 32) Test input features shape : (1860, 120, 24) Test output shape : (1860, 32)`}} > {`print('Train input features shape : {}'.format(trainX.shape)) print('\\nTrain output shape : {}'.format(trainY.shape)) print('\\nValidation input features shape : {}'.format(valX.shape)) print('\\nValidation output shape : {}'.format(valY.shape)) print('\\nTest input features shape : {}'.format(testX.shape)) print('\\nTest output shape : {}'.format(testY.shape))`}

} > {`def create_time_steps(length): return list(range(-length, 0)) def multi_step_plot(history, true_future, prediction, pic_name): plt.figure(figsize=(12, 6)) num_in = create_time_steps(len(history)) num_out = len(true_future) plt.plot(num_in, np.array(history), label='History', linewidth=2.5) plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'b', label='True Future', linewidth=2.5) if prediction.any(): plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro', label='Predicted Future', linewidth=2.5) plt.legend(loc='upper left') plt.title(pic_name) plt.show() multi_step_plot(trainX[0,:,target_index], trainY[0,:], np.array([0]), 'Train case') multi_step_plot(valX[0,:,target_index], valY[0,:], np.array([0]), 'Val case') multi_step_plot(testX[0,:,target_index], testY[0,:], np.array([0]), 'Test case')`} ### LSTM Time Series Predictor Model For more efficient storing of long sequences, here I chose the LSTM architecture. LSTM layer consists from cells which is shown below. Two vectors are inputs to the LSTM-cell: the new input vector from the data $x_t$ and the vector of the hidden state $h_{t-1}$, which is obtained from the hidden state of this cell in the previous time step.

Where: - $i_t$ - input gate - $f_t$ - forget gate - $o_t$ - output gate - $\tilde{C_t}$ - new candidate cell state - $C_t$ - cell state - $h_t$ - block output {`class LSTMTimeSeriesPredictor(nn.Module): def __init__(self, num_classes, n_features, hidden_size, num_layers, bidirectional=False, dp_rate=0.5): super(LSTMTimeSeriesPredictor, self).__init__() self.num_layers = num_layers self.hidden_size = hidden_size self.bidirectional = bidirectional self.lstm = nn.LSTM(input_size=n_features, hidden_size=hidden_size, num_layers=num_layers, dropout=dp_rate, batch_first=True, bidirectional=self.bidirectional) self.fc = nn.Linear(in_features=hidden_size, out_features=num_classes) def forward(self, x): # dim(x) = (batch, seq_len, input_size) # dim(h_0) = (num_layers *num_directions, batch, hidden_size) if self.bidirectional: h_0 = Variable(torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)) else: h_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) c_0 = Variable(torch.zeros(self.num_layers, x.size(0), self.hidden_size)) o, (h_out,_) = self.lstm(x, (h_0, c_0)) if self.bidirectional: h_out = h_out.view(self.num_layers, 2, x.size(0), self.hidden_size) # taking last hidden state # dim(h_out) = (num_directions, batch, hidden_size) h_out = h_out[-1] # in bidectional case we sum vectors over num_directions # in the case of multi-layer LSTM we sum over num_layers direction # dim(h_out) = (batch, hidden_size) h_out = h_out.sum(dim=0) # dim(out) = (batch, num_classes) out = self.fc(h_out) return out`} {`trainX = torch.from_numpy(trainX).float() trainY = torch.from_numpy(trainY).float() valX = torch.from_numpy(valX).float() valY = torch.from_numpy(valY).float() testX = torch.from_numpy(testX).float() testY = torch.from_numpy(testY).float()`} {`train_data = torch.utils.data.TensorDataset(trainX, trainY) val_data = torch.utils.data.TensorDataset(valX, valY) test_data = torch.utils.data.TensorDataset(testX, testY)`} ### Train and Evaluate Helping Functions {`def train_model(model, train_data, loss_criterion, optimizer, batch_size): running_loss = 0. model.train() train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=0) for batch_x, batch_y in train_loader: optimizer.zero_grad() outputs = model(batch_x) loss = loss_criterion(outputs, batch_y) loss.backward() optimizer.step() running_loss += loss.item() epoch_loss = running_loss / (len(train_data) // batch_size) print("Epoch: %d, train loss: %1.5f" % (epoch+1, epoch_loss)) return model, epoch_loss`} {`def evaluate_model(model, val_data, loss_criterion, optimizer, batch_size): running_loss = 0. model.eval() val_loader = torch.utils.data.DataLoader(val_data, batch_size=batch_size, shuffle=True, num_workers=0) for batch_x, batch_y in val_loader: optimizer.zero_grad() with torch.set_grad_enabled(False): outputs = model(batch_x) loss = loss_criterion(outputs, batch_y) running_loss += loss.item() epoch_loss = running_loss / (len(val_data) // batch_size) print("Epoch: %d, val loss: %1.5f" % (epoch+1, epoch_loss)) return model, epoch_loss, best_model_wts`} ### 1-Layer LSTM model {`====================1==================== Epoch: 1, train loss: 0.90311 Epoch: 1, val loss: 0.78379 Saving model for best loss ====================2==================== Epoch: 2, train loss: 0.77797 Epoch: 2, val loss: 0.73462 Saving model for best loss ====================3==================== Epoch: 3, train loss: 0.74900 Epoch: 3, val loss: 0.70943 Saving model for best loss ====================4==================== Epoch: 4, train loss: 0.73103 Epoch: 4, val loss: 0.69784 Saving model for best loss ====================5==================== Epoch: 5, train loss: 0.71739 Epoch: 5, val loss: 0.68612 Saving model for best loss ====================6==================== Epoch: 6, train loss: 0.70665 Epoch: 6, val loss: 0.67945 Saving model for best loss ====================7==================== Epoch: 7, train loss: 0.69868 Epoch: 7, val loss: 0.67938 Saving model for best loss ====================8==================== Epoch: 8, train loss: 0.69284 Epoch: 8, val loss: 0.67938 ====================9==================== Epoch: 9, train loss: 0.68811 Epoch: 9, val loss: 0.66802 Saving model for best loss ====================10==================== Epoch: 10, train loss: 0.68397 Epoch: 10, val loss: 0.67317`}} > {`learning_rate = 0.001 input_size = trainX.shape[2] # number of input features, multivariate case hidden_size = 20 num_layers = 1 num_classes = trainY.shape[1] # future time window length lstm_model = LSTMTimeSeriesPredictor(num_classes, input_size, hidden_size, num_layers, bidirectional=False) config = {'batch_size': 128, 'num_epochs': 10, 'checkpoints_dir': '/content/drive/My Drive/time series prediction/', 'model_filename': '1-layer-lstm-best_model.pth.tar'} criterion = torch.nn.MSELoss() optimizer = torch.optim.Adam(lstm_model.parameters(), lr=learning_rate) train_history, val_history = [], [] best_model_wts = copy.deepcopy(lstm_model.state_dict()) best_loss = 10e10 # for validation phase if not os.path.exists(config['checkpoints_dir']): os.mkdir(config['checkpoints_dir']) for epoch in range(config['num_epochs']): print("="*20 + str(epoch+1) + "="*20) _, train_loss = train_model(lstm_model, train_data, criterion, optimizer, config['batch_size']) train_history.append(train_loss) _, val_loss, best_model_wts = evaluate_model(lstm_model, val_data, criterion, optimizer, config['batch_size']) val_history.append(val_loss) if val_loss < best_loss: best_loss = val_loss print("Saving model for best loss") checkpoint = { 'state_dict': best_model_wts } torch.save(checkpoint, config['checkpoints_dir'] + config['model_filename']) best_model_wts = copy.deepcopy(lstm_model.state_dict())`} } > {`plt.plot(np.arange(config['num_epochs']), train_history, label='Train loss') plt.plot(np.arange(config['num_epochs']), val_history, label='Val loss') plt.xlabel("num of epochs") plt.ylabel("train loss") plt.show()`} **Test** {`lstm_model = LSTMTimeSeriesPredictor(num_classes, input_size, hidden_size, num_layers) lstm_model.load_state_dict(torch.load(os.path.join(config['checkpoints_dir'], config['model_filename']))['state_dict']) lstm_model.eval() for param in lstm_model.parameters(): param.requires_grad = False`} {[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18].map(i => ( {`Test

))} } > {`i = 0 while i < len(testX): test_x = testX[i].unsqueeze(0) test_y = testY[i].unsqueeze(0) multi_step_plot(test_x.numpy()[0, :, target_index], test_y.detach().numpy()[0, :], lstm_model(test_x).detach().numpy()[0, :], 'Test case') i += 100`} ### 2-Layer LSTM {`====================1==================== Epoch: 1, train loss: 0.89874 Epoch: 1, val loss: 0.77490 Saving model for best loss ====================2==================== Epoch: 2, train loss: 0.77985 Epoch: 2, val loss: 0.73741 Saving model for best loss ====================3==================== Epoch: 3, train loss: 0.75368 Epoch: 3, val loss: 0.71219 Saving model for best loss ====================4==================== Epoch: 4, train loss: 0.73283 Epoch: 4, val loss: 0.69299 Saving model for best loss ====================5==================== Epoch: 5, train loss: 0.71830 Epoch: 5, val loss: 0.68409 Saving model for best loss ====================6==================== Epoch: 6, train loss: 0.70771 Epoch: 6, val loss: 0.67732 Saving model for best loss ====================7==================== Epoch: 7, train loss: 0.69788 Epoch: 7, val loss: 0.67322 Saving model for best loss ====================8==================== Epoch: 8, train loss: 0.69050 Epoch: 8, val loss: 0.67194 Saving model for best loss ====================9==================== Epoch: 9, train loss: 0.68391 Epoch: 9, val loss: 0.66734 Saving model for best loss ====================10==================== Epoch: 10, train loss: 0.67748 Epoch: 10, val loss: 0.66578 Saving model for best loss`}} > {`learning_rate = 0.001 input_size = trainX.shape[2] # number of input features, multivariate case hidden_size = 20 num_layers = 2 num_classes = trainY.shape[1] # future time window length lstm_model = LSTMTimeSeriesPredictor(num_classes, input_size, hidden_size, num_layers, bidirectional=False) config = {'batch_size': 128, 'num_epochs': 10, 'checkpoints_dir': '/content/drive/My Drive/time series prediction/', 'model_filename': '2-layer-lstm-best_model.pth.tar'} criterion = torch.nn.MSELoss() optimizer = torch.optim.Adam(lstm_model.parameters(), lr=learning_rate) train_history, val_history = [], [] best_model_wts = copy.deepcopy(lstm_model.state_dict()) best_loss = 10e10 if not os.path.exists(config['checkpoints_dir']): os.mkdir(config['checkpoints_dir']) for epoch in range(config['num_epochs']): print("="*20 + str(epoch+1) + "="*20) _, train_loss = train_model(lstm_model, train_data, criterion, optimizer, config['batch_size']) train_history.append(train_loss) _, val_loss, best_model_wts = evaluate_model(lstm_model, val_data, criterion, optimizer, config['batch_size']) val_history.append(val_loss) if val_loss < best_loss: best_loss = val_loss print("Saving model for best loss") checkpoint = { 'state_dict': best_model_wts } torch.save(checkpoint, config['checkpoints_dir'] + config['model_filename']) best_model_wts = copy.deepcopy(lstm_model.state_dict())`} } > {`plt.plot(np.arange(config['num_epochs']), train_history, label='Train loss') plt.plot(np.arange(config['num_epochs']), val_history, label='Val loss') plt.xlabel("num of epochs") plt.ylabel("train loss") plt.show()`} **Test** {`lstm_model = LSTMTimeSeriesPredictor(num_classes, input_size, hidden_size, num_layers) lstm_model.load_state_dict(torch.load(os.path.join(config['checkpoints_dir'], config['model_filename']))['state_dict']) lstm_model.eval() for param in lstm_model.parameters(): param.requires_grad = False`} {[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18].map(i => ( {`2-Layer

))} } > {`i = 0 while i < len(testX): test_x = testX[i].unsqueeze(0) test_y = testY[i].unsqueeze(0) multi_step_plot(test_x.numpy()[0, :, target_index], test_y.detach().numpy()[0, :], lstm_model(test_x).detach().numpy()[0, :], 'Test case') i += 100`} ### Bidirectional 1-Layer LSTM {`====================1==================== Epoch: 1, train loss: 0.92054 Epoch: 1, val loss: 0.78858 Saving model for best loss ====================2==================== Epoch: 2, train loss: 0.77843 Epoch: 2, val loss: 0.73070 Saving model for best loss ====================3==================== Epoch: 3, train loss: 0.75058 Epoch: 3, val loss: 0.71063 Saving model for best loss ====================4==================== Epoch: 4, train loss: 0.73297 Epoch: 4, val loss: 0.69804 Saving model for best loss ====================5==================== Epoch: 5, train loss: 0.71834 Epoch: 5, val loss: 0.68801 Saving model for best loss ====================6==================== Epoch: 6, train loss: 0.70628 Epoch: 6, val loss: 0.67993 Saving model for best loss ====================7==================== Epoch: 7, train loss: 0.69642 Epoch: 7, val loss: 0.67463 Saving model for best loss ====================8==================== Epoch: 8, train loss: 0.68780 Epoch: 8, val loss: 0.67683 ====================9==================== Epoch: 9, train loss: 0.68003 Epoch: 9, val loss: 0.67213 Saving model for best loss ====================10==================== Epoch: 10, train loss: 0.67364 Epoch: 10, val loss: 0.66894 Saving model for best loss`}} > {`learning_rate = 0.001 input_size = trainX.shape[2] # number of input features, multivariate case hidden_size = 20 num_layers = 1 num_classes = trainY.shape[1] # future time window length lstm_model = LSTMTimeSeriesPredictor(num_classes, input_size, hidden_size, num_layers, bidirectional=True) config = {'batch_size': 128, 'num_epochs': 10, 'checkpoints_dir': '/content/drive/My Drive/time series prediction/', 'model_filename': 'bidir-1-layer-lstm-best_model.pth.tar'} criterion = torch.nn.MSELoss() optimizer = torch.optim.Adam(lstm_model.parameters(), lr=learning_rate) train_history, val_history = [], [] best_model_wts = copy.deepcopy(lstm_model.state_dict()) best_loss = 10e10 # for validation phase if not os.path.exists(config['checkpoints_dir']): os.mkdir(config['checkpoints_dir']) for epoch in range(config['num_epochs']): print("="*20 + str(epoch+1) + "="*20) _, train_loss = train_model(lstm_model, train_data, criterion, optimizer, config['batch_size']) train_history.append(train_loss) _, val_loss, best_model_wts = evaluate_model(lstm_model, val_data, criterion, optimizer, config['batch_size']) val_history.append(val_loss) if val_loss < best_loss: best_loss = val_loss print("Saving model for best loss") checkpoint = { 'state_dict': best_model_wts } torch.save(checkpoint, config['checkpoints_dir'] + config['model_filename']) best_model_wts = copy.deepcopy(lstm_model.state_dict())`} } > {`plt.plot(np.arange(config['num_epochs']), train_history, label='Train loss') plt.plot(np.arange(config['num_epochs']), val_history, label='Val loss') plt.xlabel("num of epochs") plt.ylabel("train loss") plt.show()`} **Test** {`from collections import OrderedDict state_dict = torch.load(os.path.join(config['checkpoints_dir'], config['model_filename']))['state_dict'] new_state_dict = OrderedDict() for k,v in state_dict.items(): if k not in ["lstm.weight_ih_l0_reverse", "lstm.weight_hh_l0_reverse", "lstm.bias_ih_l0_reverse", "lstm.bias_hh_l0_reverse"]: new_state_dict[k] = v`} {`lstm_model = LSTMTimeSeriesPredictor(num_classes, input_size, hidden_size, num_layers) lstm_model.load_state_dict(new_state_dict) lstm_model.eval() for param in lstm_model.parameters(): param.requires_grad = False`} {[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18].map(i => ( {`Bidirectional

Model	Best MSE loss
1-layer LSTM	0.66802
2-layer LSTM	0.66578
Bidirectional 1-layer LSTM	0.66894

### Summary At the first glance, all three models based on LSTM showed approximately the same results, so you can choose a single-layer LSTM without loss of quality. In cases where computational efficiency is a crucial part you may chose GRU model, which has less parameters to optimize than LSTM model. **The ways you can improve existing deep learning model:** - Work on data cleanliness, look for essential features, which are strongly related to the target variable - Manual and automatic feature engineering - Optimization of hyperparameters (length of hidden state vector, batch size, number of epochs, learning rate, number of layers) - Try different optimization algorithm - Try different loss function which will be differentiable and adequate to task you're solving - Use ensembles of prediction vectors

#lstm

Time Series Forecasting: LSTM-based model for metro traffic prediction (PyTorch)