Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection

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 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. Since real routing labels are unavailable, 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. Across 16 real-world benchmarks and four complementary evaluation metrics, TSRouter achieves the strongest overall ranking, supporting competence routing as an effective alternative to directly using foundation models as universal anomaly detectors. The code is available at https://anonymous.4open.science/r/TS-Router-D8FF.