Multiway clustering with time-varying parameters

Roy Cerqueti; Raffaele Mattera; Germana Scepi

doi:10.1007/s00180-022-01294-5

Multiway clustering with time-varying parameters

Comput Stat. 2022 Nov 1:1-42. doi: 10.1007/s00180-022-01294-5. Online ahead of print.

Authors

Roy Cerqueti^{1

2

3}, Raffaele Mattera¹, Germana Scepi⁴

Affiliations

¹ Department of Social and Economic Sciences, Sapienza University of Rome, Rome, Italy.
² School of Business, London South Bank University, London, UK.
³ GRANEM, University of Angers, Angers, France.
⁴ Department of Economics and Statistics, University of Naples "Federico II", Naples, Italy.

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: Air quality; Dynamic Conditional Score; Generalized Autoregressive Score; Multiway data; Time series clustering; time-varying parameters.