Research Article

Stock Price Prediction through STL Decomposition using Multivariate Two-way Long Short-term Memory

Authors

  • Junsuke Senoguchi Tokyo University of Technology, School of Computer Science, Department of Computer Science, Japan

Abstract

With advancements in machine-learning techniques, stock-price movements can ostensibly be forecasted using time-series data. In this study, several different types of long short-term memory (LSTM) are used to predict the closing prices of Japanese stocks five days into the future. Also, in this study, four different features [i.e., simple moving average (SMA), linear weighted moving average (WMA), exponential WMA (EMA), and the Savitzky–Golay (SG) metric] are generated from daily stock-price data and split into two components (i.e., trend and seasonal) by applying seasonal–trend decomposition using Loess (STL) decomposition. The prediction results are evaluated in terms of return, root-mean-square error (RMSE), mean absolute error (MAE), and other relevant measures of accuracy and relevancy. As a result, the multivariate two-way LSTM model yielded the highest overall performance. With respect to the RMSE and MAE of the training data, the multivariate two-way LSTM was not superior to the other models. However, with respect to RMSE and MAE on the validation data, it was the best. Also, the multivariate two-way LSTM model yielded the highest overall performance in terms of the accuracy of the direction of stock prices.

Article information

Journal

Journal of Computer Science and Technology Studies

Volume (Issue)

4 (2)

Pages

90-96

Published

2022-10-09

How to Cite

Senoguchi, J. (2022). Stock Price Prediction through STL Decomposition using Multivariate Two-way Long Short-term Memory. Journal of Computer Science and Technology Studies, 4(2), 90–96. https://doi.org/10.32996/jcsts.2022.4.2.11

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Keywords:

machine learning, multivariate bidirectional LSTM, STL decomposition, stock-price prediction