Applying a Long Short-Term Memory Approach to a Chaotic Time Series Problem – A Case Study

Abstract

For dealing with time series forecasting problems, a machine learning method with a supervised learning algorithm can be considered as an efficient alternative tool. A long short-term memory (LSTM) approach, which is an advanced deep learning model, is considered. This study applied the LSTM method using a stochastic gradient descent with momentum, an adaptive moment estimation (Adam), a root mean square propagation algorithms for forecasting a chaotic time series problem (i.e. Mackey-Glass time series problem). This study also compared the results obtained using the LSTM method with those of obtained using a back-propagation neural network (BPNN) with a scaled conjugate gradient algorithm. Experimental results show that the LSTM approach with the Adam algorithm can be used efficiently to predict the pattern of the chaotic time series, and that the best results found by using the LSTM method and the BPNN are identical. Future work will use the LSTM approach for solving stock price prediction problems in the real-world.