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Wavelets-based clustering of multivariate time series. (English) Zbl 1237.62079

Summary: Crisp and fuzzy clustering methods based on a combination of univariate and multivariate wavelet features are considered for the clustering of multivariate time series. The performance of each of these methods is evaluated for stationary and variance nonstationary multivariate time series with different error correlation structures.

The main outcomes of the simulation studies are are as follows: the superior performance of this approach for both the crisp and fuzzy cluster methods compared to some of the other approaches for clustering multivariate time series; and the very good performance of the fuzzy relational method, overall, to cluster longer time series when all of them do not appear to group exclusively into well separated clusters. We consider an application to multivariate greenhouse gases time series and show that the crisp and fuzzy clustering methods considered are well validated.

##### MSC:
 62H30 Classification and discrimination; cluster analysis (statistics) 62H86 Multivariate analysis and fuzziness 62M10 Time series, auto-correlation, regression, etc. (statistics) 65T60 Wavelets (numerical methods) 65C60 Computational problems in statistics 62P12 Applications of statistics to environmental and related topics