Time series forecasting by a seasonal support vector regression model

Authors:
Ping-Feng Pai;Kuo-Ping Lin;Chi-Shen Lin;Ping-Teng Chang
Affiliations:
Department of Information Management, National Chi Nan University, University Rd., Puli, Nantou 545, Taiwan;Department of Information Management, Lunghwa University of Science and Technology, Taoyuan 333, Taiwan;Department of Industrial Engineering and Enterprise Information, Box 985, Tunghai University, Taichung 407, Taiwan;Department of Industrial Engineering and Enterprise Information, Box 985, Tunghai University, Taichung 407, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

The support vector regression (SVR) model is a novel forecasting approach and has been successfully used to solve time series problems. However, the applications of SVR models in a seasonal time series forecasting has not been widely investigated. This study aims at developing a seasonal support vector regression (SSVR) model to forecast seasonal time series data. Seasonal factors and trends are utilized in the SSVR model to perform forecasts. Furthermore, hybrid genetic algorithms and tabu search (GA/TS) algorithms are applied in order to select three parameters of SSVR models. In this study, two other forecasting models, autoregressive integrated moving average (SARIMA) and SVR are employed for forecasting the same data sets. Empirical results indicate that the SSVR outperforms both SVR and SARIMA models in terms of forecasting accuracy. Thus, the SSVR model is an effective method for seasonal time series forecasting.