Motivated by classification issues in environmental studies, a class of hidden semi-Markov models is introduced to segment multivariate time series according to a finite number of latent regimes. The observed data are modelled by a mixture of multivariate densities, whose parameters evolve according to a latent multinomial process. The multinomial process is modelled as a semi-Markov chain where the time spent in a state and the chances of a regime- switching event are separately modeled by a battery of regression models that depend on time- varying covariates. Maximum likelihood parameter estimation is carried out by integrating an EM algorithm with a suitable data augmentation. While the proposal extends previous approaches that rely on mixtures models and hidden Markov models, it keeps a parsimonious structure that facilitates results interpretation. It is illustrated on a case study of a bivariate time series of wind and wave directions, observed by a buoy in the Adriatic sea.
26 maggio 2023
Francesco Lagona
Dept. of Political Sciences, University of Roma Tre
ore 12:00
In persona sala 34 4 piano edificio CU002 Scienze Statistiche
Webinar https://uniroma1.zoom.us/j/86881977368?pwd=SWRFc
VFjMDZTa0lXZk05TE1zNm5adz09
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