Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1015249103876
Title: ε-Descending support vector machines for financial time series forecasting
Authors: Tay, F.E.H. 
Cao, L.J.
Keywords: Non-stationary financial time series
Structural risk minimization principle
Support vector machines
Tube size
Issue Date: Apr-2002
Source: Tay, F.E.H., Cao, L.J. (2002-04). ε-Descending support vector machines for financial time series forecasting. Neural Processing Letters 15 (2) : 179-195. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1015249103876
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.
Source Title: Neural Processing Letters
URI: http://scholarbank.nus.edu.sg/handle/10635/61708
ISSN: 13704621
DOI: 10.1023/A:1015249103876
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