Multiway clustering with time-varying parameters

Roy Cerqueti, Raffaele Mattera and Germana Scepi
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Roy Cerqueti: GRANEM - Groupe de Recherche Angevin en Economie et Management - UA - Université d'Angers - Institut Agro Rennes Angers - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement

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Abstract: Abstract This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data.

Date: 2022-11-01
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Published in Computational Statistics, 2022, ⟨10.1007/s00180-022-01294-5⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04321377

DOI: 10.1007/s00180-022-01294-5

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