Complex event forecasting with prediction suffix trees

Abstract

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.