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Time series anomaly detection has been recognized as of critical importancefor the reliable and efficient operation of real-world systems. Many anomalydetection methods have been developed based on various assumptions on anomalycharacteristics. However, due to the complex nature of real-world data,different anomalies within a time series usually have diverse profilessupporting different anomaly assumptions. This makes it difficult to find asingle anomaly detector that can consistently outperform other models. In thiswork, to harness the benefits of different base models, we propose areinforcement learning-based model selection framework. Specifically, we firstlearn a pool of different anomaly detection models, and then utilizereinforcement learning to dynamically select a candidate model from these basemodels. Experiments on real-world data have demonstrated that the proposedstrategy can indeed outplay all baseline models in terms of overallperformance.

Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection