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.

基于连续时间贝叶斯网络的建模框架用于分析复杂系统中的级联行为，识别可能导致级联行为的潜在哨兵状态，并提供塑料的图形表示和可解释的结果。