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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/3223
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dc.contributor.authorTucker, A-
dc.contributor.authorSwift, S-
dc.contributor.authorLiu, X-
dc.coverage.spatial11en
dc.date.accessioned2009-04-24T11:05:27Z-
dc.date.available2009-04-24T11:05:27Z-
dc.date.issued2001-
dc.identifier.citationTucker, A., Swift, S. and Liu, X. (2001) 'Variable grouping in multivariate time series via correlation', IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 31 (2), pp.. 235 - 245. doi: 10.1109/3477.915346.en
dc.identifier.issn1083-4419-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/3223-
dc.description.abstractThe 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 methoden
dc.format.extent278413 bytes-
dc.format.extent235 - 245-
dc.format.mediumPrint-Electronic-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
dc.rights.urihttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/-
dc.subjectcorrelationen
dc.subjectevolutionary programmingen
dc.subjectgenetic algorithmsen
dc.subjectgrouping; multivariate time series (MTS)en
dc.titleVariable grouping in multivariate time series via correlationen
dc.typeResearch Paperen
dc.identifier.doihttps://doi.org/10.1109/3477.915346-
dc.relation.isPartOfIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)-
pubs.issue2-
pubs.volume31-
dc.identifier.eissn1941-0492-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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