Anlu: Enabling In-Context Time Series Anomaly Detection in Foundation Models via Counterfactual Supervision

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 another, and the query alone may not reveal which regime applies. We study in-context learning (ICL) for time series anomaly d