Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks

TL;DR

This paper introduces a framework using continuous-time Bayesian networks to analyze cascading behaviors in complex systems, identifying system states that trigger cascades and providing interpretable insights for domain experts.

Contribution

It presents a novel application of CTBNs for modeling cascade phenomena and developing methods for knowledge extraction in complex systems.

Findings

Identified likely sentry states leading to cascades

Applied methodology to industrial alarm data

Provided interpretable models for system behavior

Abstract

Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is important to understand which states of the system trigger them. For this purpose, we propose a modeling framework based on continuous-time Bayesian networks (CTBNs) to analyze cascading behavior in complex systems. This framework allows us to describe how events propagate through the system and to identify likely sentry states, that is, system states that may lead to imminent cascading behavior. Moreover, CTBNs have a simple graphical representation and provide interpretable outputs, both of which are important when communicating with domain experts. We also develop new methods for knowledge extraction from CTBNs and we apply the proposed methodology to a data set of alarms in a large industrial system.