Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

Time series anomaly detection is of critical importance for the reliable and efficient operation of real-world systems. Many anomaly detection models have been developed throughout the years based on various assumptions regarding 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, making it difficult to find a single anomaly detector that can consistently beat all other models. In this work, to harness the benefits of different base models, we assume that a pool of anomaly detection models is accessible and propose to utilize reinforcement learning to dynamically select a candidate model from these base models. Experiments on real-world data have been implemented. It is demonstrated that the proposed strategy can outperforms all baseline models in terms of overall performance.