Modeling of Multivariate Time Series Using Variable Selection and Gaussian Process

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.

关键词： Multivariate time series Gaussian process variable selection confidence intervals

作者: REN Weijie HAN Min

作者单位: Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian 116023,P.R.China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 5071-5074

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）