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

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.
978-3-030-21157-8
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/351552
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