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 miss- ing 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.

Multiway clustering with time‑varying parameters

Raffaele Mattera
;
Germana Scepi
2022

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 miss- ing 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/899253
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