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Copula-based segmentation of cylindrical time series

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  • Lagona, Francesco

Abstract

A hidden Markov model is proposed for segmenting cylindrical time series according to a finite number of latent classes, associated with copula-based cylindrical densities. It provides a parsimonious and computationally tractable approach that integrates circular–linear correlation, multimodality and temporal auto-correlation.

Suggested Citation

  • Lagona, Francesco, 2019. "Copula-based segmentation of cylindrical time series," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 16-22.
    • Handle: RePEc:eee:stapro:v:144:y:2019:i:c:p:16-22
    DOI: 10.1016/j.spl.2018.04.011
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    References listed on IDEAS

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    1. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
    2. Francesco Lagona & Marco Picone & Antonello Maruotti, 2015. "A hidden Markov model for the analysis of cylindrical time series," Environmetrics, John Wiley & Sons, Ltd., vol. 26(8), pages 534-544, December.
    3. M. Jones & Arthur Pewsey & Shogo Kato, 2015. "On a class of circulas: copulas for circular distributions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(5), pages 843-862, October.
    4. Gianluca Mastrantonio & Antonello Maruotti & Giovanna Jona‐Lasinio, 2015. "Bayesian hidden Markov modelling using circular‐linear general projected normal distribution," Environmetrics, John Wiley & Sons, Ltd., vol. 26(2), pages 145-158, March.
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    Cited by:

    1. Harvey, Andrew & Hurn, Stan & Palumbo, Dario & Thiele, Stephen, 2024. "Modelling circular time series," Journal of Econometrics, Elsevier, vol. 239(1).

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