mlsanityData to 14 Oct 2026

Online false discovery rate control for anomaly detection in time series

arXiv 2112.03196 ↗First posted 6 Dec 2021stat.ML cs.LG math.STcited by 13

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Abstract (arXiv)

This article proposes novel rules for false discovery rate control (FDRC) geared towards online anomaly detection in time series. Online FDRC rules allow to control the properties of a sequence of statistical tests. In the context of anomaly detection, the null hypothesis is that an observation is normal and the alternative is that it is anomalous. FDRC rules allow users to target a lower bound on precision in unsupervised settings. The methods proposed in this article overcome short-comings of previous FDRC rules in the context of anomaly detection, in particular ensuring that power remains high even when the alternative is exceedingly rare (typical in anomaly detection) and the test statistics are serially dependent (typical in time series). We show the soundness of these rules in both theory and experiments.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 16 of its 34 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

16 of 34 references.

References · 16

  1. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  2. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
  3. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 151
  4. 2019High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processescited by 148
  5. 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 56
  6. 2020On the Nature and Types of Anomalies: A Review of Deviations in Datacited by 22
  7. 2016Online Rules for Control of False Discovery Rate and False Discovery Exceedancecited by 21
  8. 2017Choosing Between Methods of Combining p-valuescited by 17
  9. 2017Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic datacited by 12
  10. 2017Online control of the false discovery rate with decaying memorycited by 11
  11. 2018SAFFRON: an adaptive algorithm for online control of the false discovery ratecited by 11
  12. 2019ADDIS: an adaptive discarding algorithm for online FDR control with conservative nullscited by 11
4 more
  1. 2018Asynchronous Online Testing of Multiple Hypothesescited by 10
  2. 2021Artificial Intelligence for IT Operations (AIOPS) Workshop White Papercited by 7
  3. 2015On Online Control of False Discovery Ratecited by 6
  4. 2019The Power of Batching in Multiple Hypothesis Testingcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 4; 2023: 2; 2024: 1; 2025: 5; 2026: 1.

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  1. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 250
  2. 2022ADBench: Anomaly Detection Benchmarkcited by 232
  3. 2023SimMTM: A Simple Pre-Training Framework for Masked Time-Series Modelingcited by 115
  4. 2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 25
  5. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  6. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  7. 2024CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domainscited by 8
  8. 2022A Comprehensive Review of Trends, Applications and Challenges In Out-of-Distribution Detectioncited by 5
  9. 2025Online Conformal Anomaly Detection with Prediction-Powered Data Acquisitioncited by 0
  10. 2025On the Adversarial Robustness of Learning-based Conformal Novelty Detectioncited by 0
  11. 2025Online Reliable Anomaly Detection via Neuromorphic Sensing and Communicationscited by 0
  12. 2025Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approachcited by 0
1 more
  1. 2026Safe Urban Traffic Control via Uncertainty-Aware Conformal Prediction and World-Model Reinforcement Learningcited by 0