Abstract

Editor’s note: Rapid error discovery is crucial for timely correction mechanisms and reliable router systems. Aiming to achieve a high degree of reliability, this article presents a machine-learning framework for analyzing router time series to evaluate the health status and detect anomalies while accounting for the important temporal characteristics of complex communication systems. —Paul Bogan, University of Southern California

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