Chapter

Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents

Volume 1983 of the series Lecture Notes in Computer Science pp 268-273

Date:

Feature Selection for Support Vector Machines in Financial Time Series Forecasting

  • L. J. CaoAffiliated withDepartment of Mechanical & Production Engineering, National University of Singapore
  • , Francis E. H. TayAffiliated withDepartment of Mechanical & Production Engineering, National University of Singapore

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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.