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

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.

Viewing alternatives

Metrics

Item Actions

Export

You can export this page using these formats Digital Object Identifier - DOI

About

• Item ORO ID
• 46375
• Item Type
• Journal Item
• ISSN
• 1931-6690
• Keywords
• chain graph; multiregression dynamic model; network traffic flow forecasting; gene expression networks; network data; time series
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2016 International Society for Bayesian Analysis
• Depositing User
• Catriona Queen