1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision
Tian Lan, Yifei Gao, Yimeng Lu +5
Whether a time-series pattern is anomalous often depends on the operating regime of the monitored process. A missing event can signal a fault in one regime and be routine in anothe…
Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection
Tian Lan, Yifei Gao, Yimeng Lu +6
Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of normality and favor different det…
Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing
Yifei Gao, Tian Lan, Yimeng Lu +5
Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing meth…
VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection
Yingyuan Yang, Tian Lan, Yifei Gao +6
Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundation models face a fundamental tr…