Bayesian Time Series Analysis and Forecasting

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 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 finance, environmental sciences, and the biomedical sciences.