Knowledge and Information Systems

, Volume 17, Issue 1, pp 121–133 | Cite as

Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series

  • Kaijun Wang
  • Junying Zhang
  • Fengshan Shen
  • Lingfeng Shi
Regular Paper


A dynamic Bayesian network (DBN) is one of popular approaches for relational knowledge discovery such as modeling relations or dependencies, which change over time, between variables of a dynamic system. In this paper, we propose an adaptive learning method (autoDBN) to learn DBNs with changing structures from multivariate time series. In autoDBN, segmentation of time series is achieved first through detecting geometric structures transformed from time series, and then model regions are found from the segmentation by designed finding strategies; in each found model region, a DBN model is established by existing structure learning methods; finally, model revisiting is developed to refine model regions and improve DBN models. These techniques provide a special mechanism to find accurate model regions and discover a sequence of DBNs with changing structures, which are adaptive to changing relations between multivariate time series. Experimental results on simulated and real time series show that autoDBN is very effective in finding accurate/reasonable model regions and gives lower error rates, outperforming the switching linear dynamic system method and moving window method.


Dynamic Bayesian network Adaptive learning Geometric structures of time series Changing structures Unsupervised structure learning 


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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Kaijun Wang
    • 1
  • Junying Zhang
    • 1
  • Fengshan Shen
    • 1
  • Lingfeng Shi
    • 2
  1. 1.School of Computer Science and TechnologyXidian UniversityXianPeople’s Republic of China
  2. 2.School of Electronical and Mechanical EngineeringXidian UniversityXianPeople’s Republic of China

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