Multivariate Time-series Anomaly Detection via Dynamic Model Pool & Ensembling
This addresses limitations in multi-model methods for anomaly detection in domains like service monitoring, IoT, and network security, though it is incremental as it builds on existing ensembling approaches.
The paper tackled the problem of multivariate time-series anomaly detection by proposing DMPEAD, a dynamic model pool and ensembling framework, which outperformed all baselines in experiments on 8 real-world datasets, showing superior adaptability and scalability.
Multivariate time-series (MTS) anomaly detection is critical in domains such as service monitor, IoT, and network security. While multi-model methods based on selection or ensembling outperform single-model ones, they still face limitations: (i) selection methods rely on a single chosen model and are sensitive to the strategy; (ii) ensembling methods often combine all models or are restricted to univariate data; and (iii) most methods depend on fixed data dimensionality, limiting scalability. To address these, we propose DMPEAD, a Dynamic Model Pool and Ensembling framework for MTS Anomaly Detection. The framework first (i) constructs a diverse model pool via parameter transfer and diversity metric, then (ii) updates it with a meta-model and similarity-based strategy for adaptive pool expansion, subset selection, and pool merging, finally (iii) ensembles top-ranked models through proxy metric ranking and top-k aggregation in the selected subset, outputting the final anomaly detection result. Extensive experiments on 8 real-world datasets show that our model outperforms all baselines, demonstrating superior adaptability and scalability.