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Learning and prediction of relational time series

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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.

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Authors and Affiliations

  1. DSO National Laboratories, 20 Science Park Drive, Singapore, 118230, Singapore

    Terence K. Tan

  2. The MOVES Institute, Naval Postgraduate School, 700 Dyer Road, Watkins Ext. 265, Monterey, CA, 93943-5001, USA

    Christian J. Darken

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  1. Terence K. Tan
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  2. Christian J. Darken
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Correspondence to Terence K. Tan.

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Tan, T.K., Darken, C.J. Learning and prediction of relational time series. Comput Math Organ Theory 21, 210–241 (2015). https://doi.org/10.1007/s10588-015-9182-0

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