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

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  • Horng-I Hsieh
  • Tsung-Pei Lee
  • Tian-Shyug Lee

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.

Suggested Citation

  • Horng-I Hsieh & Tsung-Pei Lee & Tian-Shyug Lee, 2011. "A Hybrid Particle Swarm Optimization and Support Vector Regression Model for Financial Time Series Forecasting," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 2(2), pages 48-56, May.
    • Handle: RePEc:jfr:ijba11:v:2:y:2011:i:2:p:48-56
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    2. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
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