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Model-based fuzzy time series clustering of conditional higher moments

Author

Listed:
  • Roy Cerqueti
  • Massimiliano Giacalone

    (UNINA - University of Naples Federico II = Università degli studi di Napoli Federico II)

  • Raffaele Mattera

    (UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome])

Abstract

This paper develops a new time series clustering procedure allowing for heteroskedasticity, non-normality and model's non-linearity. At this aim, we follow a fuzzy approach. Specifically, considering a Dynamic Conditional Score (DCS) model, we propose to cluster time series according to their estimated conditional moments via the Autocorrelation-based fuzzy -means (A-FCM) algorithm. The DCS parametric modeling is appealing because of its generality and computational feasibility. The usefulness of the proposed procedure is illustrated using an experiment with simulated data and several empirical applications with financial time series assuming both linear and nonlinear models' specification and under several assumptions about time series density function.

Suggested Citation

  • Roy Cerqueti & Massimiliano Giacalone & Raffaele Mattera, 2021. "Model-based fuzzy time series clustering of conditional higher moments," Post-Print hal-03789115, HAL.
    • Handle: RePEc:hal:journl:hal-03789115
    DOI: 10.1016/j.ijar.2021.03.011
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    Citations

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    Cited by:

    1. Roy Cerqueti & Antonio Iovanella & Raffaele Mattera, 2024. "Clustering networked funded European research activities through rank-size laws," Annals of Operations Research, Springer, vol. 342(3), pages 1707-1735, November.
    2. Raffaele Mattera & Philipp Otto, 2023. "Network log-ARCH models for forecasting stock market volatility," Papers 2303.11064, arXiv.org.
    3. Roy Cerqueti & Raffaele Mattera & Germana Scepi, 2024. "Multiway clustering with time-varying parameters," Computational Statistics, Springer, vol. 39(1), pages 51-92, February.
    4. Roy Cerqueti & Pierpaolo D’Urso & Livia Giovanni & Raffaele Mattera & Vincenzina Vitale, 2024. "Fuzzy clustering of time series based on weighted conditional higher moments," Computational Statistics, Springer, vol. 39(6), pages 3091-3114, September.
    5. Luis Lorenzo & Javier Arroyo, 2023. "Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    6. Vincenzo Basile & Massimiliano Giacalone & Paolo Carmelo Cozzucoli, 2022. "The Impacts of Bibliometrics Measurement in the Scientific Community A Statistical Analysis of Multiple Case Studies," Review of European Studies, Canadian Center of Science and Education, vol. 14(3), pages 1-10, November.
    7. Massimiliano Giacalone, 2022. "Optimal forecasting accuracy using Lp-norm combination," METRON, Springer;Sapienza Università di Roma, vol. 80(2), pages 187-230, August.
    8. Giulio Mattera & Gianfranco Piscopo & Maria Longobardi & Massimiliano Giacalone & Luigi Nele, 2024. "Improving the Interpretability of Data-Driven Models for Additive Manufacturing Processes Using Clusterwise Regression," Mathematics, MDPI, vol. 12(16), pages 1-18, August.

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