Online Detection of Cascading Change-Points Using Diffusion Networks
Online Detection of Cascading Change-Points Using Diffusion Networks
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DOI：
10.1109/allerton49937.2022.9929381
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Rui Zhang;Yao Xie;Rui Yao;Feng Qiu
中科院分区：
文献类型：
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作者：
Rui Zhang;Yao Xie;Rui Yao;Feng Qiu
我们提出了一种从序列数据中在线检测网络级联故障的方法，该方法可以建模为在短时间内发生的多个相关变化点。我们考虑了一个时间扩散网络模型来捕捉多个变化点的时间动态结构，并基于假设未知后变化分布参数的扩散网络模型，开发了一个基于广义似然比统计的顺序Shewhart过程。我们还解决了未知传播带来的计算复杂度。数值实验表明，该方法具有良好的检测串级故障的性能。
We propose an online detection procedure for cascading failures in the network from sequential data, which can be modeled as multiple correlated change-points happening during a short period. We consider a temporal diffusion network model to capture the temporal dynamic structure of multiple change-points and develop a sequential Shewhart procedure based on the generalized likelihood ratio statistics based on the diffusion network model assuming unknown post-change distribution parameters. We also tackle the computational complexity posed by the unknown propagation. Numerical experiments demonstrate good performance for detecting cascade failures.