Hidden Markov model for discrete circular-linear wind data time series

Abstract

In this work, we deal with a bivariate time series of wind speed and direction. Our observed data have peculiar features, such as informative missing values, non-reliable measures under a specific condition and interval-censored data, that we take into account in the model specification. We analyze the time series with a non-parametric Bayesian hidden Markov model, introducing a new emission distribution based on the invariant wrapped Poisson, the Poisson and the hurdle density, suitable to model our data. The model is estimated on simulated datasets and on the real data example that motivated this work.

References in corpus (4)

• Spatial analysis of wave direction data using wrapped Gaussian processes
• Bayesian Hidden Markov Modelling Using Circular-Linear General Projected Normal Distribution
• The wrapped skew Gaussian process for analyzing spatio-temporal data
• On initial direction, orientation and discreteness in the analysis of circular variables