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Dynamic Chain Graph Models for Time Series Network Data

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Original languageEnglish
Pages (from-to)491-509
JournalBayesian analysis
Issue number2
Early online date17 Jun 2016
Publication statusPublished - Jun 2017


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.

    Research areas

  • chain graph, multiregression dynamic model, network traffic flow forecasting, gene expression networks, network data, time series analyses

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