Time Series Prediction Based on Generalization Bounds for Support Vector Machine

Authors:
Liming Yang;Laisheng Wang;Yitian Xu;Qun Sun
Affiliations:
College of Science, China Agricultural University, Beijing, China 100083;College of Science, China Agricultural University, Beijing, China 100083;College of Science, China Agricultural University, Beijing, China 100083;College of Science, China Agricultural University, Beijing, China 100083
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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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.