Time series segmentation by non-homogeneous hidden semi-Markov models
:
In person.Room 34 (CU002 building, 4th floor)
Webinar^.https://uniroma1.zoom.us/j/86881977368?pwd=SWRFc
VFjMDZTa0lXZk05TE1zNm5adz09
Passcode: 432940
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.
In allegato la locandina con l'abstract e i riferimenti per partecipare al seminario.
Relatore:
Francesco Lagona
Affiliazione Relatore:
Dept. of Political Sciences, University of Roma Tre
Data:
26/05/2023 - 12:00
Allegati: