ϵ-Descending Support Vector Machines for Financial Time Series Forecasting

Authors:
Francis E. H. Tay;L. J. Cao
Affiliations:
Department of Mechanical & Production Engineering, National University of Singapore,10 Kent Ridge Crescent, 119260, Singapore. E-mail: mpetayeh@nus.edu.sg;Institute of High Performance Computing, 89C Science Park Drive #02-11/12 118261 Singapore. E-mail: caolj@ihpc.nus.edu.sg
Venue:
Neural Processing Letters
Year:
2002

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Abstract

This paper proposes a modified version of support vector machines (SVMs), called ϵ-descending support vector machines (ϵ-DSVMs), to model non-stationary financial time series. The ϵ-DSVMs are obtained by incorporating the problem domain knowledge – non-stationarity of financial time series into SVMs. Unlike the standard SVMs which use a constant tube in all the training data points, the ϵ-DSVMs use an adaptive tube to deal with the structure changes in the data. The experiment shows that the ϵ-DSVMs generalize better than the standard SVMs in forecasting non-stationary financial time series. Another advantage of this modification is that the ϵ-DSVMs converge to fewer support vectors, resulting in a sparser representation of the solution.