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Learning and prediction of relational time series

Author

Listed:
  • Terence K. Tan

    (DSO National Laboratories)

  • Christian J. Darken

    (Naval Postgraduate School)

Abstract

Learning to predict events in the near future is fundamental to human and artificial agents. Many prediction techniques are unable to learn and predict a stream of relational data online when the environments are unknown, non-stationary, and no prior training examples are available. This paper addresses the online prediction problem by introducing a low complexity learning technique called Situation Learning and several prediction techniques that use the information from Situation Learning to predict the next likely event. The prediction techniques include two variants of a Bayesian inference technique, a variable order Markov model prediction technique and situation matching techniques. We compared their prediction accuracies quantitatively for three domains: a role-playing game, computer network intrusion system alerts, and event prediction of maritime paths in a discrete-event simulator.

Suggested Citation

  • Terence K. Tan & Christian J. Darken, 2015. "Learning and prediction of relational time series," Computational and Mathematical Organization Theory, Springer, vol. 21(2), pages 210-241, June.
    • Handle: RePEc:spr:comaot:v:21:y:2015:i:2:d:10.1007_s10588-015-9182-0
    DOI: 10.1007/s10588-015-9182-0
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    References listed on IDEAS

    as
    1. Patrick Jungkunz & Christian J. Darken, 2011. "A computational model for human eye-movements in military simulations," Computational and Mathematical Organization Theory, Springer, vol. 17(3), pages 229-250, September.
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    Cited by:

    1. Bradley J. Best & William G. Kennedy & Robert St. Amant, 2015. "Behavioral representation in modeling and simulation: introduction to CMOT special issue—BRiMS 2012," Computational and Mathematical Organization Theory, Springer, vol. 21(3), pages 243-246, September.

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