2005 | OriginalPaper | Buchkapitel
A Study of Modelling Non-stationary Time Series Using Support Vector Machines with Fuzzy Segmentation Information
verfasst von : Shaomin Zhang, Lijia Zhi, Shukuan Lin
Erschienen in: Computational Intelligence and Security
Verlag: Springer Berlin Heidelberg
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We present a new approach for modelling non-stationary time series, which combines multi-SVR and fuzzy segmentation. Following the idea of Janos Abonyi [11] where an algorithm of fuzzy segmentation was applied to time series, in this article we modify it and unite the segmentation and multi-SVR with a heuristic weighting on
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. Experimental results showing its practical viability are presented.