- Bayesian Anal.
- Volume 12, Number 2 (2017), 491-509.
Dynamic Chain Graph Models for Time Series Network Data
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.
Bayesian Anal., Volume 12, Number 2 (2017), 491-509.
First available in Project Euclid: 17 June 2016
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Anacleto, Osvaldo; Queen, Catriona. Dynamic Chain Graph Models for Time Series Network Data. Bayesian Anal. 12 (2017), no. 2, 491--509. doi:10.1214/16-BA1010. https://projecteuclid.org/euclid.ba/1466165926
- Supplementary material for paper: Dynamic chain graph models for time series network data. Supplementary material available online includes the theorem for which Corollary 1 is a consequence, together with the proofs of that theorem and Corollary 1. It also includes the description and results of the application of the DCGM to two gene expression datasets, as mentioned in Section 5.