mlsanityData to 14 Oct 2026

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

arXiv 2610.16219 ↗First posted 12 Oct 2026cs.AI cs.LGcited by 0

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Abstract (arXiv)

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 detector from it. Specifically, we score each detector on the confirmed anomaly, and those scores become the weights of a soft mixture over the whole pool. We show that a single confirmed anomaly is enough to come close to the best detector for the series. Across 152 univariate and 73 multivariate series, ONESHOT outperforms the strongest single detector, averaging the whole pool, and even picking the best detector on the confirmed event. Code is available at: https://github.com/seunghan96/one_shot.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 12 Oct 2026.

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24 of 54 references.

References · 24

  1. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  2. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  3. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  4. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 482
  5. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  6. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  7. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  8. 2023FITS: Modeling Time Series with 10k Parameterscited by 195
  9. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
  10. 2019Deep Anomaly Detection with Deviation Networkscited by 153
  11. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 151
  12. 2020COPOD: Copula-Based Outlier Detectioncited by 146
12 more
  1. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  2. 2018Extended Isolation Forestcited by 81
  3. 2018LSCP: Locally Selective Combination in Parallel Outlier Ensemblescited by 32
  4. 2016How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?cited by 24
  5. 2022Toward Unsupervised Outlier Model Selectioncited by 19
  6. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 7
  7. 2023Open-Set Multivariate Time-Series Anomaly Detectioncited by 4
  8. 2026Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoringcited by 4
  9. 2026Mitigating Label Shift in Tabular In-Context Learning via Test-Time Posterior Adjustmentcited by 4
  10. 2025Channel Normalization for Time Series Channel Identificationcited by 3
  11. 2026EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Seriescited by 3
  12. 2026RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithmscited by 1

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