Title: Forecasting time series data using moving-window swarm intelligence-optimised machine learning regression
Authors: Ngoc-Tri Ngo; Thi Thu Ha Truong
Addresses: Faculty of Project Management, The University of Danang – University of Science and Technology, 54 Nguyen Luong Bang Street, Da Nang City, Vietnam ' Department of Civil Engineering, The University of Danang – University of Technology and Education, 48 Cao Thang Street, Da Nang City, Vietnam
Abstract: This study proposes a hybrid time series forecast model namely a moving-window firefly algorithm (FA)-based least squares support vector regression (MFA-LSSVR). In the proposed model, the LSSVR captures patterns of historical data and predicts future values of time series data while the FA is used to optimise the LSSVR`s parameters to improve the predictive accuracy. The proposed model was trained and tested using two actual datasets of the daily energy demand data and the stock price data. Experimental results show that the proposed MFA-LSSVR model is effective in forecasting time series data and the comparison results revealed that the proposed model outperforms other models, i.e., the LSSVR and the ARIMA (autoregressive integrated moving average) in predicting energy demand and stock price. This study's findings, thus, provide decision makers a potential approach in early forecasting future patterns of time series data.
Keywords: machine learning regression; moving-window concept; swarm intelligence; time series forecast.
International Journal of Intelligent Engineering Informatics, 2019 Vol.7 No.5, pp.422 - 440
Received: 17 Jan 2019
Accepted: 28 Apr 2019
Published online: 08 Nov 2019 *