Dynamic chain graph models for time series network data

Anacleto, Osvaldo and Queen, Catriona (2017). Dynamic chain graph models for time series network data. Bayesian Analysis, 12(2) pp. 491–509.

DOI: https://doi.org/10.1214/16-BA1010


This paper introduces a new class of Bayesian dynamic models for inference and forecasting in high-dimensional time series observed on networks. The new model, called the dynamic chain graph model, is suitable for multivariate time series which exhibit symmetries within subsets of series and a causal drive mechanism between these subsets. The model can accommodate high-dimensional, non-linear and non-normal time series and enables local and parallel computation by decomposing the multivariate problem into separate, simpler sub-problems of lower dimensions. The advantages of the new model are illustrated by forecasting traffic network flows and also modelling gene expression data from transcriptional networks.

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