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.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2019
- DOI:
- 10.48550/arXiv.1911.05610
- arXiv:
- arXiv:1911.05610
- Bibcode:
- 2019arXiv191105610Z
- Keywords:
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- Statistics - Other Statistics;
- Electrical Engineering and Systems Science - Signal Processing;
- Electrical Engineering and Systems Science - Systems and Control
- E-Print:
- 6 pages