Multiagent bayesian forecasting of structural time-invariant dynamic systems with graphical models
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 0888-613XFull text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.ijar.2010.07.004
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|
|Number of Pages:||18|
|Page Range:||pp. 960-977|
|Access rights to Published version:||Restricted or Subscription Access|
Actions (login required)
Downloads per month over past year