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2005 | OriginalPaper | Chapter

A Study of Modelling Non-stationary Time Series Using Support Vector Machines with Fuzzy Segmentation Information

Authors : Shaomin Zhang, Lijia Zhi, Shukuan Lin

Published in: Computational Intelligence and Security

Publisher: 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

ε

. Experimental results showing its practical viability are presented.

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Metadata
Title
A Study of Modelling Non-stationary Time Series Using Support Vector Machines with Fuzzy Segmentation Information
Authors
Shaomin Zhang
Lijia Zhi
Shukuan Lin
Copyright Year
2005
Publisher
Springer Berlin Heidelberg
Book
Computational Intelligence and Security

Print ISBN: 978-3-540-30818-8

Electronic ISBN: 978-3-540-31599-5

Copyright Year: 2005

DOI
https://doi.org/10.1007/11596448_76

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