Feature Selection for Support Vector Machines in Financial Time Series Forecasting

  • L. J. Cao
  • Francis E. H. Tay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1983)


This paper deals with the application of saliency analysis to Support Vector Machines (SVMs) for feature selection. The importance of feature is ranked by evaluating the sensitivity of the network output to the feature input in terms of the partial derivative. A systematic approach to remove irrelevant features based on the sensitivity is developed. Five futures contracts are examined in the experiment. Based on the simulation results, it is shown that that saliency analysis is effective in SVMs for identifying important features.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • L. J. Cao
    • 1
  • Francis E. H. Tay
    • 1
  1. 1.Department of Mechanical & Production EngineeringNational University of SingaporeSingapore

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