NDLI: Generic phase space reconstruction method of multivariate time series

Content Provider	IEEE Xplore Digital Library
Author	Lingshuang Kong Chunhua Yang Yalin Wang Weihua Gui
Copyright Year	2009
Description	Author affiliation: School of Information Science and Engineering, Central South University, Changsha 410083, China (Lingshuang Kong; Chunhua Yang; Yalin Wang; Weihua Gui)
Abstract	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.
Starting Page	3752
Ending Page	3755
File Size	507928
Page Count	4
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5191737
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Information science Art Multivariate time series Space technology Phase space reconstruction Rough sets Reconstruction algorithms Predictive models Educational institutions
Content Type	Text
Resource Type	Article

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