Xiang, Yang, Smith, James and Kroes, Jeff. (2011) Multiagent bayesian forecasting of structural time-invariant dynamic systems with graphical models. International Journal of Approximate Reasoning, Vol.52 (No.7). pp. 960-977. ISSN 0888-613X

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Abstract

Time series are found widely in engineering and science. We study forecasting of stochastic, dynamic systems based on observations from multivariate time series. We model the domain as a dynamic multiply sectioned Bayesian network (DMSBN) and populate the domain by a set of proprietary, cooperative agents. We propose an algorithm suite that allows the agents to perform one-step forecasts with distributed probabilistic inference. We show that as long as the DMSBN is structural time-invariant (possibly parametric time-variant), the forecast is exact and its time complexity is exponentially more efficient than using dynamic Bayesian networks (DBNs). In comparison with independent DBN-based agents, multiagent DMSBNs produce more accurate forecasts. The effectiveness of the framework is demonstrated through experiments on a supply chain testbed.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions:	Faculty of Science > Centre for Scientific Computing
Journal or Publication Title:	International Journal of Approximate Reasoning
Publisher:	Elsevier
ISSN:	0888-613X
Date:	2011
Volume:	Vol.52
Number:	No.7
Number of Pages:	18
Page Range:	pp. 960-977
Identification Number:	10.1016/j.ijar.2010.07.004
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
URI:	http://wrap.warwick.ac.uk/id/eprint/39805

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