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
;
2020-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
