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Indexed by:会议论文

Date of Publication:2014-07-28

Included Journals:EI、CPCI-S、SCIE、Scopus

Page Number:5071-5074

Key Words:Multivariate time series; Gaussian process; variable selection; confidence intervals

Abstract:A complete learning framework for modeling multivariate time series is presented in this paper. First, in order to construct input variables, variable selection method based on max dependency criterion is introduced, which can remove redundant and irrelevant variables. Then, Gaussian process model is adopted as prediction model, which has powerful capability of nonlinear modeling. In addition, confidence and confidence intervals are built for the evaluation of predictive results. Finally, the model is applied to the prediction of real world multivariate time series. The simulation results show the effectiveness and practicality of the proposed method.