Multiway clustering with time-varying parameters.
Journal article
Cerqueti, R., Mattera, R. and Scepi, G. (2022). Multiway clustering with time-varying parameters. Computational statistics. https://doi.org/10.1007/s00180-022-01294-5
Authors | Cerqueti, R., Mattera, R. and Scepi, G. |
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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. [Abstract copyright: © The Author(s) 2022.] |
Keywords | Multiway data; Air quality; Dynamic Conditional Score; Time series clustering; time-varying parameters; Generalized Autoregressive Score |
Year | 2022 |
Journal | Computational statistics |
Publisher | Springer |
ISSN | 0943-4062 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00180-022-01294-5 |
https://doi.org/1294 | |
Publication dates | |
Online | 01 Nov 2022 |
Publication process dates | |
Accepted | 06 Oct 2022 |
Deposited | 21 Nov 2022 |
Publisher's version | License File Access Level Open |
Page range | 1-42 |
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https://openresearch.lsbu.ac.uk/item/929x2
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