Abstract
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.
Citation
Osvaldo Anacleto. Catriona Queen. "Dynamic Chain Graph Models for Time Series Network Data." Bayesian Anal. 12 (2) 491 - 509, June 2017. https://doi.org/10.1214/16-BA1010
Information