Time Series Anomaly Detection Via Reinforcement Learning-based Model Selection

Abstract

Time series anomaly detection has been recognized as of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection methods have been developed based on various assumptions on anomaly characteristics. However, due to the complex nature of real-world data, different anomalies within a time series usually have diverse profiles supporting different anomaly assumptions. This makes it difficult to find a single anomaly detector that can consistently outperform other models. In this work, to harness the benefits of different base models, we propose a reinforcement learning-based model selection framework. Specifically, we first learn a pool of different anomaly detection models, and then utilize reinforcement learning to dynamically select a candidate model from these base models. Experiments on real-world data have demonstrated that the proposed strategy can indeed outplay all baseline models in terms of overall performance.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyzhang2022time

Related papers

• Sequential Anomaly Detection Using Inverse Reinforcement Learning (2020)0.00
• A Scalable Algorithm For Anomaly Detection Via Learning-based Controlled Sensing (2021)0.00
• Anomaly Detection Under Controlled Sensing Using Actor-critic Reinforcement Learning (2020)0.00
• Scalable And Decentralized Algorithms For Anomaly Detection Via Learning-based Controlled Sensing (2021)0.00
• Anomaly Detection Via Learning-based Sequential Controlled Sensing (2023)5.84
• Anomaly Detection For Scalable Task Grouping In Reinforcement Learning-based RAN Optimization (2023)0.00
• Improved Training Mechanism For Reinforcement Learning Via Online Model Selection (2025)0.00
• OIL-AD: An Anomaly Detection Framework For Sequential Decision Sequences (2024)0.00