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.
Multiway clustering with time-varying parameters / Cerqueti, Roy; Mattera, Raffaele; Scepi, Germana. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - (2022). [10.1007/s00180-022-01294-5]
Multiway clustering with time-varying parameters
Roy Cerqueti;Raffaele Mattera
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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 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.File | Dimensione | Formato | |
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