Wavelets-based clustering of multivariate time series

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; 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.