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Multiway clustering with time-varying parameters

Author

Listed:
  • Roy Cerqueti

    (Sapienza University of Rome
    London South Bank University
    University of Angers)

  • Raffaele Mattera

    (Sapienza University of Rome)

  • Germana Scepi

    (University of Naples “Federico II”)

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.

Suggested Citation

  • Roy Cerqueti & Raffaele Mattera & Germana Scepi, 2024. "Multiway clustering with time-varying parameters," Computational Statistics, Springer, vol. 39(1), pages 51-92, February.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-022-01294-5
    DOI: 10.1007/s00180-022-01294-5
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    References listed on IDEAS

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    1. Siem Jan Koopman & André Lucas & Marcel Scharth, 2016. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 97-110, March.
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    3. Roy Cerqueti & L. de Giovanni & P. d'Urso & M. Giacalone & R. Mattera, 2022. "Weighted score-driven fuzzy clustering of time series with a financial application," Post-Print hal-03789065, HAL.
    4. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
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    8. Roy Cerqueti & M. Giacalone & R. Mattera, 2021. "Model-based fuzzy time series clustering of conditional higher moments," Post-Print hal-03789115, HAL.
    9. Katarina Košmelj & Vladimir Batagelj, 1990. "Cross-sectional approach for clustering time varying data," Journal of Classification, Springer;The Classification Society, vol. 7(1), pages 99-109, March.
    10. Harvey, Andrew & Sucarrat, Genaro, 2014. "EGARCH models with fat tails, skewness and leverage," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 320-338.
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