Multiagent Bayesian Forecasting of Structural Time-Invariant Dynamic Systems with Graphical Models

dc.contributor.authorXiang, Yang
dc.contributor.authorSmith, James
dc.contributor.authorKroes, Jeff
dc.date.accessioned2015-06-19T19:55:18Z
dc.date.available2015-06-19T19:55:18Z
dc.date.issued2011
dc.degree.departmentSchool of Computer Scienceen
dc.description.abstractTime 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.en_US
dc.description.sponsorshipNSERC, Canadaen_US
dc.identifier.issn0888-613X
dc.identifier.urihttp://hdl.handle.net/10214/8931
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectTime seriesen_US
dc.subjectforecastingen_US
dc.subjectprobabilistic inferenceen_US
dc.subjectmultiagent systemsen_US
dc.subjectgraphical modelsen_US
dc.subjectBayesian networksen_US
dc.subjectmultiply sectioned Bayesian networksen_US
dc.titleMultiagent Bayesian Forecasting of Structural Time-Invariant Dynamic Systems with Graphical Modelsen_US
dc.typeArticleen_US
dcterms.relationY. Xiang, J. Smith and J. Kroes, Multiagent Bayesian Forecasting of Structural Time-Invariant Dynamic Systems with Graphical Models. International Journal of Approximate Reasoning, Vol.52, No. 7, 960-977, 2011.

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