The proposed methodology integrates extreme value theory, graph analysis, and graph neural networks to identify the origins of critical system failures. The approach aims to determine the timing, propagation paths, and initial source of events like market crashes or mechanical failures.
A specialized agent automates the diagnostic pipeline, replacing disparate analytical tools with a unified, reproducible workflow. This system maps how a localized anomaly spreads through interconnected channels to eventually trigger a system-wide extreme event.
The research includes a historical overview of extreme value theory development spanning nearly a century. The author details the practical implementation of this multi-step diagnostic framework and the underlying mathematical principles used to detect system instability.
