Simulation and Empirical Studies of Long Short-Term Memory Performance to Deal with Limited Data

Abstrak

This research is proposed to determine the performance of time series machine learning in the presence of noise, where this approach is intended to forecast time series data. The approach method chosen is long short-term memory (LSTM), a development of recurrent neural network (RNN). Another problem is the availability of data, which is not limited to high-dimensional data but also limited data. Therefore, this study tests the performance of long short-term memory using simulated data, where the simulated data used in this study are data generated from the functional autoregressive (FAR) model and data generated from the functional autoregressive model of order 1 FAR(1) which is given additional noise. Simulation results show that the long short-term memory method in analyzing time series data in the presence of noise outperforms by 1-5% the method without noise and data with limited observations. The best performance of the method is determined by testing the analysis of variance against the mean absolute percentage error. In addition, the empirical data used in this study are the percentage of poverty, unemployment, and economic growth in Java. The method that has the best performance in analyzing each poverty data is used to forecast the data. The comparison result for the empirical data is that the M-LSTM method outperforms the LSTM in analyzing the poverty percentage data. The best method performance is determined based on the average value of the mean absolute percentage error of 1-10%.