Multivariate Time-series Anomaly Detection via Dynamic Model Pool & Ensembling

• URL: http://arxiv.org/abs/2601.02037v1
• Date: Mon, 05 Jan 2026 11:48:21 GMT
• Title: Multivariate Time-series Anomaly Detection via Dynamic Model Pool & Ensembling
• Authors: Wei Hu, Zewei Yu, Jianqiu Xu,
• Abstract summary: We propose DMPEAD, a Dynamic Model Pool and Ensembling framework for MTS Anomaly Detection.<n>We show that our model outperforms all baselines, demonstrating superior adaptability and scalability.
• Score: 10.831568759799657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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