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.
May, 26, 2023
Francesco Lagona
Dept. of Political Sciences, University of Roma Tre
at noon
In person room 34 4th floor building CU002 Sctatistical Sciences
Webinar https://uniroma1.zoom.us/j/86881977368?pwd=SWRFc
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