A new hidden Markov model is proposed for the analysis of cylindrical time series, i.e. bivariate time series of intensities and angles. It allows to segment cylindrical time series according to a finite number of regimes that represent the conditional distributions of the data under specific environmental conditions. The model parsimoniously accommodates for circular-linear correlation, multimodality, skewness and temporal autocorrelation. A computationally efficient Expectation-Maximization algorithm is described to estimate the parameters and a parametric bootstrap routine is provided to compute confidence intervals. These methods are illustrated on cylindrical time series of wave heights and directions.
LAGONA F, PICONE M, & MARUOTTI A (2015). A Hidden Markov model for the analysis of cylindrical time series. ENVIRONMETRICS, 26, 534-544 [10.1002/env.2355].
Titolo: | A Hidden Markov model for the analysis of cylindrical time series | |
Autori: | ||
Data di pubblicazione: | 2015 | |
Rivista: | ||
Citazione: | LAGONA F, PICONE M, & MARUOTTI A (2015). A Hidden Markov model for the analysis of cylindrical time series. ENVIRONMETRICS, 26, 534-544 [10.1002/env.2355]. | |
Abstract: | A new hidden Markov model is proposed for the analysis of cylindrical time series, i.e. bivariate time series of intensities and angles. It allows to segment cylindrical time series according to a finite number of regimes that represent the conditional distributions of the data under specific environmental conditions. The model parsimoniously accommodates for circular-linear correlation, multimodality, skewness and temporal autocorrelation. A computationally efficient Expectation-Maximization algorithm is described to estimate the parameters and a parametric bootstrap routine is provided to compute confidence intervals. These methods are illustrated on cylindrical time series of wave heights and directions. | |
Handle: | http://hdl.handle.net/11590/119703 | |
Appare nelle tipologie: | 1.1 Articolo in rivista |