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 52emes Journees de Statistiques de la Societe Francaise de Statistique (SFdS) Recueil des soumissions, 494-499. (pp.494-499).

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.
Lagona, F., Maruotti, A. (2020). A hidden semi-Markov model for segmenting environmental toroidal data. In 52emes Journees de Statistiques de la Societe Francaise de Statistique (SFdS) Recueil des soumissions, 494-499. (pp.494-499).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/385470
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