Multiway clustering with time-varying parameters

Roy Cerqueti (), Raffaele Mattera () and Germana Scepi ()
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Roy Cerqueti: Sapienza University of Rome
Raffaele Mattera: Sapienza University of Rome
Germana Scepi: University of Naples “Federico II”

Computational Statistics, 2024, vol. 39, issue 1, No 4, 92 pages

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.

Keywords: Generalized Autoregressive Score; Dynamic Conditional Score; time-varying parameters; Time series clustering; Multiway data; Air quality (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01294-5

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