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- W2380004598 abstract "Certain deterministic non-linear systems may show chaotic behavior. Time series derived from such systems seem stochastic. Sensitivity of the chaotic system to initial conditional impedes long-term predictions of time series. However, it is possible to make short-term predictions by exploiting the determinism. In this paper, a novel nonlinear prediction technique of using support vector machines (SVM) based on Statistical Learning Theory (SLT) has been proposed as well as adaptive optimization method of SVM parameters. The SVM achieves higher generalization performances than traditional regression techniques. Simulation results show the approach has better potential in the filed of chaotic time series nonlinear prediction. The effects of free parameters of SVM and embedding dimension on generalization error also have been analyzed and obtained conclusions seem consistent with the theory of VC dimension derived from SLT." @default.
- W2380004598 created "2016-06-24" @default.
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- W2380004598 date "2005-01-01" @default.
- W2380004598 modified "2023-09-26" @default.
- W2380004598 title "Chaotic Time Series Nonlinear Prediction Based on Support Vector Machines" @default.
- W2380004598 hasPublicationYear "2005" @default.
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