Generic Phase Space Reconstruction Method of Multivariate Time Series

摘要：

In order to obtain the effective input vector for the prediction of multivariate time series, a generic phase space reconstruction method combining classical reconstruction technology with reduction theory of rough sets was proposed. Firstly, the embedding dimension was determined by minimizing the mean one-step prediction error and the original reconstruction phase space was obtained. Then, the original decision-table with redundant embedding dimensions for multivariate time series was set up and the RS reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space. Finally, the samples were extracted according to generic reconstruction results to identify the parameters of prediction model. Verification results show that the developed reconstruction method leads better generalization ability for the prediction model and it is feasible and worthwhile for application.

关键词： Multivariate time series Phase space reconstruction Rough sets

作者: Lingshuang Kong Chunhua Yang Yalin Wang Weihua Gui

作者单位: School of Information Science and Engineering, Central South University, Changsha 410083, China Coll School of Information Science and Engineering, Central South University, Changsha 410083, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 3752-3755

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）