# Wayeb: a Tool for Complex Event Forecasting

arXiv: https://arxiv.org/abs/1901.01826 · HTML: https://mlsanity.com/paper/1901.01826/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2018.

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

- [Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report](https://mlsanity.com/paper/2109.00287/) (2021)
- [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)
- [Information Cascade Prediction under Public Emergencies: A Survey](https://mlsanity.com/paper/2404.01319/) (2024)

## Abstract (arXiv)

Complex Event Processing (CEP) systems have appeared in abundance during the last two decades. Their purpose is to detect in real-time interesting patterns upon a stream of events and to inform an analyst for the occurrence of such patterns in a timely manner. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CEP engine. We present Wayeb, a tool that attempts to address the issue of Complex Event Forecasting. Wayeb employs symbolic automata as a computational model for pattern detection and Markov chains for deriving a probabilistic description of a symbolic automaton.