# Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report

arXiv: https://arxiv.org/abs/2109.00287 · HTML: https://mlsanity.com/paper/2109.00287/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 61 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [Constructing Narrative Event Evolutionary Graph for Script Event Prediction](https://mlsanity.com/paper/1805.05081/) (2018)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Wayeb: a Tool for Complex Event Forecasting](https://mlsanity.com/paper/1901.01826/) (2018)
- [Event Forecasting with Pattern Markov Chains](https://mlsanity.com/paper/1804.10388/) (2018)
- [Online Event Recognition from Moving Vessel Trajectories](https://mlsanity.com/paper/1601.06041/) (2016)
- [Probabilistic Complex Event Recognition: A Survey](https://mlsanity.com/paper/1702.06379/) (2017)
- [Symbolic Automata with Memory: a Computational Model for Complex Event Processing](https://mlsanity.com/paper/1804.09999/) (2018)

## Built on by (most cited first, 4 of 4)

- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Learning Automata-Based Complex Event Patterns in Answer Set Programming](https://mlsanity.com/paper/2208.14820/) (2022)
- [Complex Event Recognition with Symbolic Register Transducers: Extended Technical Report](https://mlsanity.com/paper/2407.02884/) (2024)
- [Symbolic Register Automata for Complex Event Recognition and Forecasting](https://mlsanity.com/paper/2110.04032/) (2021)

## Abstract (arXiv)

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. Our experimental results demonstrate the benefits, in terms of accuracy, of being able to capture such long-term dependencies. This is achieved by increasing the order of our model beyond what is possible with full-order Markov models that need to perform an exhaustive enumeration of all possible past sequences of a given order. We also discuss extensively how CEF solutions should be best evaluated on the quality of their forecasts.