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.
It supports zero-shot anomaly scoring using masked-token reconstruction error and is pretrained on a broad corpus including anomaly detection datasets (Liu & Paparrizos, 2024)
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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.