# Wayeb: a Tool for Complex Event Forecasting

## TL;DR

Wayeb is a novel tool that enhances complex event processing by enabling the forecasting of pattern occurrences using symbolic automata and Markov chains, addressing a gap in predictive capabilities.

## Contribution

It introduces a new approach combining symbolic automata and Markov chains for forecasting complex events before detection.

## Key findings

- Demonstrates effective prediction of event patterns
- Integrates automata and probabilistic models for forecasting
- Addresses a key gap in real-time event processing

## Abstract

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.