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OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection

L5 · ResearcherResearcharXiv· 10/12/2026

Novel technique for ML researchers working on label-efficient anomaly detection in time series data.

AI Summary

Researchers propose OneShot, a method that uses just one labeled anomaly to optimally weight multiple time series anomaly detectors, outperforming single detectors and averaging approaches.

Excerpt

Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existing methods therefore either select one detector for each series or average a pool of them. However, both assume that no label ever arrives, while a deployed monitor does receive them, since an operator confirms the alarms it raises. In this paper, we propose ONESHOT, which uses a single confirmed anomaly to weight the pool rather than to choose one

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