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Complex event forecasting with prediction suffix trees


Complex event recognition (CER) systems have become popular in the past two decades due to their ability to “instantly” detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of complex event forecasting (CEF). Our framework combines two formalisms: (a) symbolic automata which are used to encode complex event patterns and (b) prediction suffix trees which can provide a succinct probabilistic description of an automaton’s behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. We also discuss how CEF solutions should be best evaluated on the quality of their forecasts.

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This work has received funding from the EU Horizon 2020 research and innovation program INFORE under Grant Agreement No. 825070.

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Correspondence to Elias Alevizos.

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Alevizos, E., Artikis, A. & Paliouras, G. Complex event forecasting with prediction suffix trees. The VLDB Journal (2021).

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  • Finite automata
  • Regular expressions
  • Complex event recognition
  • Complex event processing
  • Symbolic automata
  • Variable-order Markov models