A model-agnostic adaptive conformal anomaly detection approach uses weighted quantile bounds learned from past foundation model predictions to deliver interpretable p-value scores with stable calibration under shifts for time series monitoring.
V olume under the surface: a new accuracy evaluation measure for time-series anomaly detection.Proceedings of the VLDB Endowment, 15(11):2774–2787, 2022a
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Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
A model-agnostic adaptive conformal anomaly detection approach uses weighted quantile bounds learned from past foundation model predictions to deliver interpretable p-value scores with stable calibration under shifts for time series monitoring.