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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.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1214/16-BA1010
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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.

Item Type: Journal Item
Copyright Holders: 2016 International Society for Bayesian Analysis
ISSN: 1931-6690
Keywords: chain graph; multiregression dynamic model; network traffic flow forecasting; gene expression networks; network data; time series
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 46375
Depositing User: Catriona Queen
Date Deposited: 06 Jun 2016 12:34
Last Modified: 17 May 2017 10:28
URI: http://oro.open.ac.uk/id/eprint/46375
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