Feature Selection for Support Vector Machines in Financial Time Series Forecasting

  • L. J. Cao
  • Francis E. H. Tay
Conference paper

DOI: 10.1007/3-540-44491-2_38

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1983)
Cite this paper as:
Cao L.J., Tay F.E.H. (2000) Feature Selection for Support Vector Machines in Financial Time Series Forecasting. In: Leung K.S., Chan LW., Meng H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations