Toroidal time series are temporal sequences of bivariate angular observations that often arise in environmental and ecological studies. A hidden semi- Markov model is proposed for segmenting these data according to a finite number of latent classes, associated 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.

Lagona, F., Maruotti, A. (2020). A hidden semi-Markov model for segmenting environmental toroidal data. In N.S. Alessio Pollice (a cura di), Book of Short Papers SIS 2020 (pp. 810-815). Pearson.

A hidden semi-Markov model for segmenting environmental toroidal data

Francesco Lagona
;
2020

Abstract

Toroidal time series are temporal sequences of bivariate angular observations that often arise in environmental and ecological studies. A hidden semi- Markov model is proposed for segmenting these data according to a finite number of latent classes, associated 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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Lagona, F., Maruotti, A. (2020). A hidden semi-Markov model for segmenting environmental toroidal data. In N.S. Alessio Pollice (a cura di), Book of Short Papers SIS 2020 (pp. 810-815). Pearson.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/374605
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