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