ε-Descending Support Vector Machines for Financial Time Series Forecasting
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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.
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- ε-Descending Support Vector Machines for Financial Time Series Forecasting
Neural Processing Letters
Volume 15, Issue 2 , pp 179-195
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- non-stationary financial time series
- support vector machines
- tube size
- structural risk minimization principle
- Industry Sectors