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Hidden Markov Models for multivariate functional data

Author

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  • Martino, Andrea
  • Guatteri, Giuseppina
  • Paganoni, Anna Maria

Abstract

In this paper we extend the usual Hidden Markov Models framework, where the observed objects are univariate or multivariate data, to the case of functional data, by modeling the temporal structure of a system of multivariate curves evolving in time.

Suggested Citation

  • Martino, Andrea & Guatteri, Giuseppina & Paganoni, Anna Maria, 2020. "Hidden Markov Models for multivariate functional data," Statistics & Probability Letters, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:stapro:v:167:y:2020:i:c:s0167715220302200
    DOI: 10.1016/j.spl.2020.108917
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    References listed on IDEAS

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    1. Paas, L.J. & Vermunt, J.K. & Bijmolt, T.H.A., 2007. "Discrete-time discrete-state latent Markov modelling for assessing and predicting household acquisitions of financial products," Other publications TiSEM 5781ab33-6687-4ad5-b57a-3, Tilburg University, School of Economics and Management.
    2. Leonard J. Paas & Jeroen K. Vermunt & Tammo H. A. Bijmolt, 2007. "Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 955-974, October.
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    Cited by:

    1. Marius Ötting & Dimitris Karlis, 2023. "Football tracking data: a copula-based hidden Markov model for classification of tactics in football," Annals of Operations Research, Springer, vol. 325(1), pages 167-183, June.

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