Motivated by classification issues in marine studies, we propose a hidden semi-Markov model to segment toroidal time series according to a finite number of latent regimes. The time spent in a given regime and the chances of a regime switching event are separately modeled by a battery of regression models that depend on time-varying covariates.
Lagona, F., Mingione, M. (2023). Segmenting toroidal time series by nonhomogeneous hidden semi-Markov models. In G.G. Pietro Coretto (a cura di), CLADAG 2023 Book of abstracts and short papers (pp. 197-200). Pearson Education Resources.
Segmenting toroidal time series by nonhomogeneous hidden semi-Markov models
Francesco Lagona
;Marco Mingione
2023-01-01
Abstract
Motivated by classification issues in marine studies, we propose a hidden semi-Markov model to segment toroidal time series according to a finite number of latent regimes. The time spent in a given regime and the chances of a regime switching event are separately modeled by a battery of regression models that depend on time-varying covariates.File in questo prodotto:
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