Toroidal time series are temporal sequences of bivariate angular observations that often arise in environmental and ecological studies. A hidden Markov model is proposed for segmenting these data according to a finite number of latent classes, associated with copula-based toroidal densities. The model conveniently integrates circular correlation, multimodality and temporal auto-correlation. A computationally efficient EM algorithm is proposed for parameter estimation. The proposal is illustrated on a time series of wind and sea wave directions.
Lagona, F. (2019). A Copula-Based Hidden Markov Model for Toroidal Time Series. In F.R.a.R.V. Alessandra Petrucci (a cura di), New Statistical Developments in Data Science (pp. 435-446). Springer International Publishing [10.1007/978-3-030-21158-5_32].
A Copula-Based Hidden Markov Model for Toroidal Time Series
Lagona, Francesco
2019-01-01
Abstract
Toroidal time series are temporal sequences of bivariate angular observations that often arise in environmental and ecological studies. A hidden Markov model is proposed for segmenting these data according to a finite number of latent classes, associated with copula-based toroidal densities. The model conveniently integrates circular correlation, multimodality and temporal auto-correlation. A computationally efficient EM algorithm is proposed for parameter estimation. The proposal is illustrated on a time series of wind and sea wave directions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
