Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection
This work addresses the challenge of selecting the best anomaly detection model for complex real-world time series data, which is incremental as it builds on existing methods by adding a selection mechanism.
The paper tackles the problem of time series anomaly detection by proposing a reinforcement learning-based model selection framework to dynamically choose among different base models, resulting in outperforming all baseline models in overall performance on real-world data.
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.