LSTM-based Multivariate Time-Series Analysis: A Case of Journal Visitors Forecasting

Abstract

Forecasting is the process of predicting something in the future based on previous patterns. Forecasting will never be 100% accurate because the future has a problem of uncertainty. However, using the right method can make forecasting have a low error rate value to provide a good forecast for the future. This study aims to determine the effect of increasing the number of hidden layers and neurons on the performance of the long short-term memory (LSTM) forecasting method. LSTM performance measurement is done by root mean square error (RMSE) in various architectural scenarios. The LSTM algorithm is considered capable of handling long-term dependencies on its input and can predict data for a relatively long time. Based on research conducted from all models, the best results were obtained with an RMSE value of 0.699 obtained in model 1 with the number of hidden layers 2 and 64 neurons. Adding the number of hidden layers can significantly affect the RMSE results using neurons 16 and 32 in Model 1.