Explainable Graph Ensemble Learning for Multivariate Time Series Anomaly Detection in Cloud Microservice Architectures
Year: 2025
Type: article
Abstract: The increasing adoption of cloud microservice architectures demands robust multivariate time series anomaly detection algorithms to ensure a reliable computing infrastructure. Although countless of these algorithms are readily available in the literature, significant concerns remain about their interpretability, particularly in cases where it is required to consider both the temporal and topological aspects of the explanation. Our proposed framework leverages attention-based spatial-temporal graph convolutional networks and ensemble learning to capture complex spatial-temporal dependencies across time series. Our approach achieves an averaged event-wise F1 score of 0.94 on two proprietary datasets. After the detection phase, an analysis of the network attention weights, at carefully selected layers, provides useful explanations of the anomalous events to cloud operators.
Authors: Kevin O’Shea, Sen Yan, Ming Yu, Xianjuan Chen, Stefano Mauceri, Bhaskar Dhariyal, Lei Xu, Noel O’Connor, Mingming Liu
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W4416366558