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) |
Issue 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 |
Files in This Item:
File | Description | Size | Format | |
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Variable Grouping Multivariate Time Series via Correlation.pdf | 271.89 kB | Adobe PDF | View/Open |
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