🤖 AI Summary
This work proposes GDME, a novel framework addressing the limitations of existing online anomaly detection methods in industrial systems, where heterogeneous streaming time series exhibit diverse and rapidly evolving patterns. GDME dynamically maintains a pool of models, constructs a graph to capture inter-model relationships, and adaptively selects an optimal subset for ensemble learning through community detection. By monitoring structural changes in the graph, the framework effectively detects concept drift and enhances adaptability to data evolution. Integrating unsupervised learning, dynamic graph modeling, and online ensemble strategies, GDME achieves up to a 24% average performance improvement over state-of-the-art online methods and static ensemble baselines across seven heterogeneous datasets, while maintaining favorable computational efficiency.
📝 Abstract
With the increasing volume of streaming data in industrial systems, online anomaly detection has become a critical task. The diverse and rapidly evolving data patterns pose significant challenges for online anomaly detection. Many existing anomaly detection methods are designed for offline settings or have difficulty in handling heterogeneous streaming data effectively. This paper proposes GDME, an unsupervised graph-based framework for online time series anomaly detection using model ensemble. GDME maintains a dynamic model pool that is continuously updated by pruning underperforming models and introducing new ones. It utilizes a dynamic graph structure to represent relationships among models and employs community detection on the graph to select an appropriate subset for ensemble. The graph structure is also used to detect concept drift by monitoring structural changes, allowing the framework to adapt to evolving streaming data. Experiments on seven heterogeneous time series demonstrate that GDME outperforms existing online anomaly detection methods, achieving improvements of up to 24%. In addition, its ensemble strategy provides superior detection performance compared with both individual models and average ensembles, with competitive computational efficiency.