Iterated time series prediction with multiple support vector regression models

Authors:
Li Zhang;Wei-Da Zhou;Pei-Chann Chang;Ji-Wen Yang;Fan-Zhang Li
Affiliations:
Research Center of Machine Learning and Data Analysis, School of Computer Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China;AI Speech Ltd., Suzhou 215123, Jiangsu, China;Department of Information Management, Yuan Ze University, Taoyuan 32026, Taiwan, China;Research Center of Machine Learning and Data Analysis, School of Computer Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China;Research Center of Machine Learning and Data Analysis, School of Computer Science and Technology, Soochow University, Suzhou 215006, Jiangsu, China
Venue:
Neurocomputing
Year:
2013

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Abstract

Support vector regression (SVR) model has been widely applied to time series prediction. In general iterative methods, the multi-step-ahead prediction is based on the iteration of the exact one-step prediction. Even if the one-step prediction model is very exact, the iteration procedure would accumulate prediction errors when repeating one-step-ahead prediction, which results in bad prediction performance. This paper deals with iterated time series prediction problem by using multiple SVR models, which are trained independently based on the same training data with different targets. In other words, the n-th SVR model performs an n-step-ahead prediction. The prediction outputs of these models are considered as the next input state variables to perform further prediction. Since each SVR model is an exact prediction model, the accumulate prediction error would be reduced by using multiple SVR models possibly. Actually, the MSVR method can be taken as a compromise of the iterative method and the direct method. Experimental results on time series data show that the multiple SVR model method is effective.