Published In
2025 Conference on AI, Science, Engineering, and Technology (aixset)
Document Type
Pre-Print
Publication Date
2025
Subjects
Time series data
Abstract
Anomaly detection in time series data is important for applications in finance, healthcare, sensor networks, and industrial monitoring. Traditional methods usually struggle with limited labeled data, high false-positive rates, and difficulty generalizing to novel anomaly types. To overcome these challenges, we propose a reinforcement learning-based framework that integrates dynamic reward shaping, Variational Autoencoder (VAE), and active learning, called DRTA. Our method uses an adaptive reward mechanism that balances exploration and exploitation by dynamically scaling the effect of VAE-based reconstruction error and classification rewards. This approach enables the agent to detect anomalies effectively in low-label systems while maintaining high precision and recall. Our experimental results on the Yahoo A1 and Yahoo A2 benchmark datasets demonstrate that the proposed method consistently outperforms state-of-theart unsupervised and semi-supervised approaches. These findings show that our framework is a scalable and efficient solution for real-world anomaly detection tasks.11Code is available at GitHub Repository.
Rights
© Copyright the author(s) 2026
DOI
10.1109/AIxSET65682.2025.00009
Persistent Identifier
https://archives.pdx.edu/ds/psu/44696
Publisher
IEEE
Citation Details
Published as: Golchin, B., Rekabdar, B., & Liu, K. (2025). DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection. In (Editor), 2025 Conference on AI, Science, Engineering, and Technology (AIxSET). https://doi.org/10.1109/aixset65682.2025.00009
Description
This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published as: (2025). DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection. In (Editor), 2025 Conference on AI, Science, Engineering, and Technology (AIxSET). https://doi.org/10.1109/aixset65682.2025.00009