Bayesian Forecasting of Multivariate Dynamic Models

An overview  of recent research advances in Bayesian state-space modelling of multivariate time series is presented.

The focus is on concepts that enables application of state-space models to increasingly large-scale data,  applying to continuous or discrete time series outcomes.

The scope includes: i) a review of dynamic models and operational aspects,  ii)  large time varying parameters vector autoregressive models and  iii) dynamic dependence network models.