|
Abstract:
|
This short-course covers basic principles and methods of Bayesian dynamic modeling in time series analysis and forecasting, with methodological details of central model classes explored in a range of examples. A main focus is on dynamic linear models— structure, inference, forecasting— including stationary and non-stationary time series and volatility modelling. Following detailed coverage and examples of univariate time series analysis, the course extends to linked systems of univariate series defining specific classes of multivariate models, and goes further in multivariate contexts with dynamic factor models. Aspects of simulation-based computation—forward simulation for forecasting, forward-backward simulation for analysis of state-space models, and MCMC methods for models with parameters and latent states going beyond the linear/Gaussian framework—are included. The course draws on a range of examples from business, finance, signal processing, environmental sciences, and the biomedical sciences.
|
