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RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning

arXiv 2104.00543 ↗First posted 31 Mar 2021cs.LG cs.CLcited by 13

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Abstract (arXiv)

We introduce a new semi-supervised, time series anomaly detection algorithm that uses deep reinforcement learning (DRL) and active learning to efficiently learn and adapt to anomalies in real-world time series data. Our model - called RLAD - makes no assumption about the underlying mechanism that produces the observation sequence and continuously adapts the detection model based on experience with anomalous patterns. In addition, it requires no manual tuning of parameters and outperforms all state-of-art methods we compare with, both unsupervised and semi-supervised, across several figures of merit. More specifically, we outperform the best unsupervised approach by a factor of 1.58 on the F1 score, with only 1% of labels and up to around 4.4x on another real-world dataset with only 0.1% of labels. We compare RLAD with seven deep-learning based algorithms across two common anomaly detection datasets with up to around 3M data points and between 0.28% to 2.65% anomalies.We outperform all of them across several important performance metrics.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 12 of its 42 references that are in the map. First posted on arXiv 31 Mar 2021.

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12 of 42 references.

References · 12

  1. 2014Semi-Supervised Learning with Deep Generative Modelscited by 494
  2. 2017Deep Reinforcement Learning: An Overviewcited by 327
  3. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  4. 2015Semi-Supervised Learning with Ladder Networkscited by 246
  5. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  6. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
  7. 2019Deep Anomaly Detection with Deviation Networkscited by 152
  8. 2017Good Semi-supervised Learning that Requires a Bad GANcited by 85
  9. 2018Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detectioncited by 79
  10. 2014Toward Supervised Anomaly Detectioncited by 73
  11. 2020Sequential Anomaly Detection using Inverse Reinforcement Learningcited by 19
  12. 2015An unsupervised spatiotemporal graphical modeling approach to anomaly detection in distributed CPScited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 2; 2025: 4; 2026: 1.

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  1. 2022Task-Relevant Failure Detection for Trajectory Predictors in Autonomous Vehiclescited by 11
  2. 2021Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot Learningcited by 4
  3. 2025Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learningcited by 3
  4. 2025Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policiescited by 2
  5. 2024Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learningcited by 1
  6. 2022Label-Efficient Interactive Time-Series Anomaly Detectioncited by 0
  7. 2023Infrastructure-based End-to-End Learning and Prevention of Driver Failurecited by 0
  8. 2023Tackling Diverse Minorities in Imbalanced Classificationcited by 0
  9. 2023Expert enhanced dynamic time warping based anomaly detectioncited by 0
  10. 2024An Active Learning Framework with a Class Balancing Strategy for Time Series Classificationcited by 0
  11. 2025DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detectioncited by 0
  12. 2025Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approachcited by 0
1 more
  1. 2026BoundAD: Boundary-Aware Negative Generation for Time Series Anomaly Detectioncited by 0