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Online detection of a cascade of multiple change-points

[Submitted on 28 Oct 2019 (v1), last revised 20 Oct 2020 (this version, v2)]

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Abstract: We propose novel online detection of a cascading failure in the network using
sequential measurement data. Our goal is to detect the failure as quickly as
possible after it occurs. To achieve this goal, we propose a temporal diffusion
network model representing the dynamics of the potential changes to help us
capture the changes as quickly as possible. In cascading failures, once the
failure affects a node, its measurements change from a pre-change distribution
to an unknown post-change distribution. We develop sequential generalized
likelihood ratio statistics that perform joint detection and estimation in
detecting the change. Numerical experiments show that our method outperforms
existing methods.

Submission history

From: Rui Zhang [view email]

Mon, 28 Oct 2019 19:19:42 UTC (93 KB)

Tue, 20 Oct 2020 19:55:05 UTC (623 KB)

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