A hybrid support vector machines and discrete wavelet transform model in futures price forecasting

  • Authors:

  • Fan-yong Liu;Min Fan

  • Affiliations:

  • Financial Engineering Research Center, South China University of Technology, Wushan, Guangzhou, P.R. China;Financial Engineering Research Center, South China University of Technology, Wushan, Guangzhou, P.R. China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

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Abstract

This paper is motivated by evidence that different forecasting models can complement each other in approximating data sets, and presents a hybrid model of support vector machines (SVMs) and discrete wavelet transform (DWT) to solve the futures prices forecasting problems. The presented model greatly improves the prediction performance of the single SVMs model in forecasting futures prices. In our experiment, the performance of the hybrid is evaluated using futures prices. Experimental results indicate that the hybrid model outperforms the individual SVMs models in terms of root mean square error (RMSE) metric. This hybrid model yields better forecasting result than the SVMs model.