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.
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Zhang, S., Zhi, L., Lin, S. (2005). A Study of Modelling Non-stationary Time Series Using Support Vector Machines with Fuzzy Segmentation Information. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_76
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DOI: https://doi.org/10.1007/11596448_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
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