Copula-based segmentation of environmental time series with linear and circular components
Francesco Lagona · Iris (Roma Tre University) · 2017
A novel segmentation method is proposed for the analysis of bivariate times eries of intensities and angles that often occur in environmental applications. The model is based on a mixture of copula-based cylindrical distributions,whose parameters evolve according to a latent Markov chain.The model parsimoniously accommodates typical features of cylindrical time series such as circular-linear correlation, multimodality, skewness and temporal auto-correlation. A computationally efficient Expectation-Maximization algorithm is described to estimate the parameters and a parametric bootstrap routine is exploited to compute confidence intervals. These methods are illustrated on cylindrical time series of wave heights and directions in the Adriatic sea.