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.

翻译：多元时间序列（MTS）异常检测在服务监控、物联网和网络安全等领域至关重要。尽管基于模型选择或集成的方法优于单一模型方法，但仍存在以下局限：（i）选择方法依赖单一选定模型且对策略敏感；（ii）集成方法通常融合所有模型或仅限于单变量数据；（iii）大多数方法依赖固定数据维度，限制了可扩展性。为解决这些问题，我们提出DMPEAD——一种用于多元时间序列异常检测的动态模型池与集成框架。该框架首先（i）通过参数迁移与多样性度量构建多样化模型池；随后（ii）利用元模型与基于相似度的策略进行自适应池扩展、子集选择及池融合以更新模型池；最终（iii）在选定子集中通过代理度量排序与top-k聚合对排名靠前的模型进行集成，输出最终异常检测结果。在8个真实数据集上的大量实验表明，本模型优于所有基线方法，展现出卓越的适应性与可扩展性。