Bayesian Inference on Latent Structure in Time Series
Omar Aguilar, Gabriel Huerta, Raquel Prado, Mike West · 1999
Abstract A range of developments in Bayesian time series modelling in recent years has focussed on issues of identifying latent structure in time series. This has led to new uses and interpretations of existing theory for latent process decompositions of dynamic models, and to new models for univariate and multivariate time series. This article draws together concepts and modelling approaches that are central to applications of time series decomposition methods, and reviews recent modelling and applied developments. Several applications in time series analyses in geology, climatology, psychiatry and finance are discussed, as are related modelling directions and current research frontiers.