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Article Dans Une Revue Computational Statistics Année : 2022

Multiway clustering with time-varying parameters

Raffaele Mattera
Germana Scepi
  • Fonction : Auteur

Résumé

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.

Dates et versions

hal-04321377 , version 1 (04-12-2023)

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Roy Cerqueti, Raffaele Mattera, Germana Scepi. Multiway clustering with time-varying parameters. Computational Statistics, 2022, ⟨10.1007/s00180-022-01294-5⟩. ⟨hal-04321377⟩

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