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Anacleto, Osvaldo and Queen, Catriona
(2017).
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.
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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