Feature selection for support vector machines in financial time series forecasting

14Citations
Citations of this article
112Readers
Mendeley users who have this article in their library.
Get full text

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.

Cite

CITATION STYLE

APA

Cao, L. J., & Tay, F. E. H. (2000). Feature selection for support vector machines in financial time series forecasting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1983, pp. 268–273). Springer Verlag. https://doi.org/10.1007/3-540-44491-2_38

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free