Neural Processing Letters

, Volume 15, Issue 2, pp 179–195 | Cite as

ε-Descending Support Vector Machines for Financial Time Series Forecasting

  • Francis E. H. Tay
  • L. J. Cao


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.

non-stationary financial time series support vector machines tube size structural risk minimization principle 


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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Francis E. H. Tay
    • 1
  • L. J. Cao
    • 2
  1. 1.Department of Mechanical & Production EngineeringNational University of SingaporeSingapore.
  2. 2.Institute of High Performance ComputingSingapore

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