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Time Series Prediction Based on Generalization Bounds for Support Vector Machine

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Advances in Neural Networks – ISNN 2009 (ISNN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5552))

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Abstract

The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive model (AR) concerns many important fields. The Vapnik-Chervonenkis (VC) generalization bound provides a mathematical framework for the practical models selection from finite and noisy data sets of time series dataset. In this paper, based on the VC generalization bound for Support Vector Machine (SVM), we introduce a new method of identifying the time varying parameters of an AR model, then and two SVM-based time series prediction models are formulated. Both numerical experiments and theoretical analysis show that the proposed models are feasible and effective.

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References

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Authors and Affiliations

  1. College of Science, China Agricultural University, Beijing, 100083, China

    Liming Yang, Laisheng Wang, Yitian Xu & Qun Sun

Authors
  1. Liming Yang
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  2. Laisheng Wang
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  3. Yitian Xu
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  4. Qun Sun
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Editor information

Editors and Affiliations

  1. Departamento de Control Automático,, CINVESTAV-IPN,, A.P. 14-740, Av.IPN 2508,, D.F., 07360,, México, México

    Wen Yu

  2. Deptartment of Electrical and Computer Engineering,, Stevens Institute of Technology,, NJ 07030,, Hoboken,, USA

    Haibo He

  3. Dept. of Electrical and Computer Engineering,, South Dakota School of Mines & Technology,, 501 E. St. Joseph Street,, SD 57701,, Rapid City,, USA

    Nian Zhang

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© 2009 Springer-Verlag Berlin Heidelberg

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Yang, L., Wang, L., Xu, Y., Sun, Q. (2009). Time Series Prediction Based on Generalization Bounds for Support Vector Machine. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_37

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  • DOI: https://doi.org/10.1007/978-3-642-01510-6_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01509-0

  • Online ISBN: 978-3-642-01510-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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