Online detection of cascading change-points

Abstract

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 the good performance for detecting cascade failures.

arXiv comment6 pages

References in corpus (6)

• Uncovering the Temporal Dynamics of Diffusion Networks
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• Quickest Change Detection of a Markov Process Across a Sensor Array
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Cited by in corpus (1)

• Quickest Inference of Network Cascades with Noisy Information