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2005 Clustering Time Series, Subspace Identification and Cepstral Distances
Jeroen Boets, Katrien De Cock, Marcelo Espinoza, Bart De Moor
Commun. Inf. Syst. 5(1): 69-96 (2005).

Abstract

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.

Citation

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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.

Information

Published: 2005
First available in Project Euclid: 7 June 2006

zbMATH: 1089.62103
MathSciNet: MR2199724

Rights: Copyright © 2005 International Press of Boston

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Vol.5 • No. 1 • 2005
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