Abstract
Toroidal time series are temporal sequences of bivariate angular observations that often arise in environmental and ecological studies. A hidden Markov model is proposed for segmenting these data according to a finite number of latent classes, associated with copula-based toroidal densities. The model conveniently integrates circular correlation, multimodality and temporal auto-correlation. A computationally efficient EM algorithm is proposed for parameter estimation. The proposal is illustrated on a time series of wind and sea wave directions.
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Acknowledgements
Francesco Lagona was supported by the 2015 PRIN supported project ‘Environmental processes and human activities: capturing their interactions via statistical methods’, funded by the Italian Ministry of Education, University and Scientific Research.
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Lagona, F. (2019). A Copula-Based Hidden Markov Model for Toroidal Time Series. In: Petrucci, A., Racioppi, F., Verde, R. (eds) New Statistical Developments in Data Science. SIS 2017. Springer Proceedings in Mathematics & Statistics, vol 288. Springer, Cham. https://doi.org/10.1007/978-3-030-21158-5_32
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DOI: https://doi.org/10.1007/978-3-030-21158-5_32
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