Nonhomogeneous hidden semi-Markov models for toroidal data

2023/12/22 by Francesco Lagona, Lagona, Francesco, Marco Mingione +1 · 2 citations
Engineering · Environmental Science · #Applications (stat.AP) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Methodology (stat.ME) #Wind and Air Flow Studies

paper · pdf · doi:10.48550/arxiv.2312.14719

openalex publication_date 2023/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A nonhomogeneous hidden semi-Markov model is proposed to segment toroidal time series according to a finite number of latent regimes and, simultaneously, estimate the influence of time-varying covariates on the process' survival under each regime. The model is a mixture of toroidal densities, whose parameters depend on the evolution of a semi-Markov chain, which is in turn modulated by time-varying covariates through a proportional hazards assumption. Parameter estimates are obtained using an EM algorithm that relies on an efficient augmentation of the latent process. The proposal is illustrated on a time series of wind and wave directions recorded during winter.

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• Flexible unimodal density estimation in hidden Markov models
• Environmental Risk Assessment via Nonhomogeneous Hidden Semi-Markov Models with Penalized Vector Auto-Regression

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