Advertisement

A Hybrid Support Vector Machines and Discrete Wavelet Transform Model in Futures Price Forecasting

  • Fan-yong Liu
  • Min Fan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

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.

Keywords

Support Vector Machine Root Mean Square Error Radial Basis Function Hybrid Model Discrete Wavelet Transform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hall, J.W.: Adaptive Selection of US Stocks with Neural Nets. In: Deboeck, G.J. (ed.) Trading On the Edge. Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets, pp. 45–65. John Wiley & Sons, New York (1994)Google Scholar
  2. 2.
    Vapnik, V.N.: The Nature of Statistical Learning Theory, 1st edn. Springer, Heidelberg (1995)MATHGoogle Scholar
  3. 3.
    Tay, F.E.H., Li-Juan, C.: Application of Support Vector Machines in Financial Time Series Forecasting. The International Journal of Management Science 29, 309–317 (2001)Google Scholar
  4. 4.
    Tay, F.E.H., Li-Juan, C.: Modified Support Vector Machines in Financial Time Series Forecasting. Neurocomputing 48, 847–861 (2002)MATHCrossRefGoogle Scholar
  5. 5.
    Peter, Z.G.: Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing 50, 159–175 (2003)MATHCrossRefGoogle Scholar
  6. 6.
    Ping-Feng, P., Chih-Sheng, L.: A Hybrid ARIMA and Support Vector Machines Model in Stock Price Forecasting. The International Journal of Management Science 33, 497–505 (2005)Google Scholar
  7. 7.
    Yousefi, S., Weinreich, I., Reinarz, D.: Wavelet-Based Prediction of Oil Prices. Chaos, Solitons and Fractals 25, 265–275 (2005)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fan-yong Liu
    • 1
  • Min Fan
    • 1
  1. 1.Financial Engineering Research CenterSouth China University of TechnologyWushanP.R. China

Personalised recommendations