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.
CITATION STYLE
Lagona, F. (2019). A copula-based hidden markov model for toroidal time series. In Springer Proceedings in Mathematics and Statistics (Vol. 288, pp. 435–446). Springer New York LLC. https://doi.org/10.1007/978-3-030-21158-5_32
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