发表机构
Tsinghua University; Huawei; Datadog AI Research（清华大学; 华为; Datadog AI 研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出TS-Router框架，利用预训练时间序列表示估计各异常检测器能力并自适应选择专家，无需目标标签，在16个基准上取得最佳平均排名。
AI 中文摘要
时间序列异常检测（TSAD）难以跨数据集泛化，因为异质的时间动态意味着不同的正常性概念，并偏好不同的检测标准。虽然时间序列基础模型提供了可迁移的表示，但将它们与固定的异常评分机制耦合可能会忽视这种差异。这促使我们对基于基础模型的TSAD采取不同的视角：利用基础模型来协调专门的异常标准，而不是直接强加一个通用标准。基于这一观点，我们提出了\ extbf{TS-Router}，一个通用表示、专用检测框架，它从预训练的时间表示中估计异质异常检测器的相对能力，并为每个目标序列选择合适的专家。为了避免依赖真实任务中的专家性能标签，我们从专家在标记的模拟任务上的相对表现中推导出软能力监督。在部署时，路由不需要目标异常标签，仅选定的专家在目标序列上进行无监督拟合。我们在表示覆盖和条件能力稳定性下界定了Top-\(k\)集合能力遗憾。在16个真实世界基准和四个互补评估指标上，TS-Router取得了最佳的整体平均排名。使用多个冻结的TSFM编码器的受控消融进一步支持使用预训练表示进行能力估计和自适应专家选择。代码可在该https URL获取。
英文摘要
Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of normality and favor different detection criteria. While time-series foundation models provide transferable representations, coupling them with a fixed anomaly-scoring mechanism can overlook this variation. This motivates a different perspective on foundation-model-based TSAD: using foundation models to coordinate specialized anomaly criteria rather than directly imposing a universal one. Based on this view, we propose \textbf{TS-Router}, a generalist-representation, specialist-detection framework that estimates the relative competence of heterogeneous anomaly detectors from pretrained temporal representations and selects suitable specialists for each target series. To avoid relying on specialist-performance labels from real tasks, we derive soft competence supervision from specialists' relative performance on labeled simulated tasks. At deployment, routing requires no target anomaly labels, and only the selected specialists are fitted unsupervisedly on the target series. We bound Top-\(k\) set-competence regret under representation coverage and conditional competence stability. Across 16 real-world benchmarks and four complementary evaluation metrics, TS-Router achieves the best overall average rank. Controlled ablations with multiple frozen TSFM encoders further support the use of pretrained representations for competence estimation and adaptive specialist selection. The code is available at https://anonymous.4open.science/r/TS-Router-D8FF.
