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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3223

Title: Variable grouping in multivariate time series via correlation
Authors: Tucker, A
Swift, S
Liu, X
Keywords: Correlation
evolutionary programming
genetic algorithms
grouping; multivariate time series (MTS)
Publication Date: 2001
Publisher: IEEE
Citation: Systems, Man, and Cybernetics, Part B, IEEE Transactions on. 31 (2) 235-245
Abstract: The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the “variable groupings” problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping method
URI: http://bura.brunel.ac.uk/handle/2438/3223
ISSN: 1083-4419
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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