A Comparison of Efficiency Improvement for Long Short-Term Memory Model Using Convolutional Operations and Convolutional Neural Network

Abstract

This work studied the comparison of LSTM, Con-vLSTM and CNN-LSTM model, that was applied for time series forecasting. We created the LSTM, CNN-LSTM, ConvLSTM model and configured the optimal parameters by using hyperparameters optimization techniques. All models were applied to two different datasets for forecasting the number of patients in the future. This work also applied the SeLu and ReLu activation function to avoid the problem of gradient vanishing and improve the self-normalizing. The results indicated that two models had skillful, and made the reliable forecasting in two datasets. This work benchmarked the model performance by calculating MAE, RMSE, and sMAPE, which was acceptable in all case study. The CNN-LSTM model with SeLu activation function gave highest forecasting efficiency for the data contain seasonal variation. LSTM model with SeLu activation function gave highest forecasting efficiency in the case of non-stationary data.