# Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks

arXiv: https://arxiv.org/abs/2308.10606 · HTML: https://mlsanity.com/paper/2308.10606/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 47 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Builds on (most cited first, 2 of 2)

- [Sequential change-point detection for mutually exciting point processes over networks](https://mlsanity.com/paper/2102.05724/) (2021)
- [A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networks](https://mlsanity.com/paper/2007.03248/) (2020)

## Abstract (arXiv)

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.