mlsanityData to 14 Oct 2026

Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning

arXiv 2504.02999 ↗First posted 3 Apr 2025cs.LGcited by 3

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional methods often struggle with manual parameter tuning and cannot adapt to new anomaly types. Our method overcomes these limitations by integrating Deep Reinforcement Learning (DRL) with a Variational Autoencoder (VAE) and Active Learning. By incorporating a Long Short-Term Memory (LSTM) network, our approach models sequential data and its dependencies effectively, allowing for the detection of new anomaly classes with minimal labeled data. Our innovative DRL- VAE and Active Learning combination significantly improves existing methods, as shown by our evaluations on real-world datasets, enhancing anomaly detection techniques and advancing time series analysis.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 30 references that are in the map. First posted on arXiv 3 Apr 2025.

Builds on

6 of 30 references.

References · 6

  1. 2017Deep Reinforcement Learning: An Overviewcited by 333
  2. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  3. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
  4. 2021RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learningcited by 13
  5. 2018Object-sensitive Deep Reinforcement Learningcited by 10
  6. 2025Emotion Detection in Twitter Messages Using Combination of Long Short-Term Memory and Convolutional Deep Neural Networkscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2026LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detectioncited by 1
  2. 2025DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detectioncited by 0
  3. 2025Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approachcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning
  2. 2024An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework
  3. 2026CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection
  4. 2024Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection
  5. 2020Sequential Anomaly Detection using Inverse Reinforcement Learning
  6. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
  7. 2024Towards efficient deep autoencoders for multivariate time series anomaly detection
  8. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection