Using dynamic chain graphs to model high-dimensional time series: an application to real-time traffic flow forecasting

This seminar will show how the dynamic chain graph model can deal with the ever-increasing problems of inference and forecasting when analysing high-dimensional time series. The dynamic chain graph model is a new class of Bayesian dynamic models suitable for multivariate time series which exhibit symmetries between subsets of series and a causal drive mechanism between these subsets. This model can accommodate non-linear and non-normal time series and simplifies computation by decomposing a multivariate problem into separate, simpler sub-problems of lower dimensions. An example of its application using real-time multivariate traffic flow data as well as potential applications of the model in other areas will be also discussed.