A study of modelling non-stationary time series using support vector machines with fuzzy segmentation information

Authors:
Shaomin Zhang;Lijia Zhi;Shukuan Lin
Affiliations:
College of Information and Engineering, Northeastern University, Shenyang, China;College of Information and Engineering, Northeastern University, Shenyang, China;College of Information and Engineering, Northeastern University, Shenyang, China
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

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 ε. Experimental results showing its practical viability are presented.