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论文类型：会议论文

发表时间：2014-07-28

收录刊物：EI、CPCI-S、SCIE、Scopus

页面范围：5071-5074

关键字：Multivariate time series; Gaussian process; variable selection; confidence intervals

摘要：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.