In this paper a methodology to cluster time series based on measurement data is described. In particular, we propose a distance for stochastic models based on the concept of subspace angles within a model and between two models. This distance is used to obtain a clustering over the set of time series. We show how it is related to the mutual information of the past and the future output processes, and to a previously defined cepstral distance. Finally, the methodology is applied to the clustering of time series of power consumption within the Belgian electricity grid.
Jeroen Boets. Katrien De Cock. Marcelo Espinoza. Bart De Moor. "Clustering Time Series, Subspace Identification and Cepstral Distances." Commun. Inf. Syst. 5 (1) 69 - 96, 2005.