A Hybrid Particle Swarm Optimization and Support Vector Regression Model for Financial Time Series Forecasting

Horng-I Hsieh, Tsung-Pei Lee and Tian-Shyug Lee

International Journal of Business Administration, 2011, vol. 2, issue 2, 48-56

Abstract: In this paper, a time series forecasting approach by integrating particle swarm optimization (PSO) and support vector regression (SVR) is proposed. SVR has been widely applied in time series predictions. However, no general guidelines are available to choose the free parameters of an SVR model. The proposed approach uses PSO to search the optimal parameters for model selections in the hope of improving the performance of SVR. In order to evaluate the performance of the proposed approach, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) closing cash index is used as the illustrative example. Experimental results show that the proposed model outperforms the traditional SVR model and provides an alternative in financial time series forecasting.

Keywords: Data mining; Particle swarm optimization; Support vector regression; Time series forecasting (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:jfr:ijba11:v:2:y:2011:i:2:p:48-56

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