Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

Jiuqi Elise Zhang; Di Wu; Benoit Boulet

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arXiv · 2205.09884

Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

Jiuqi Elise Zhang·Di Wu·Benoit Boulet

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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.

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BibTeX RIS

Jiuqi Elise Zhang, Di Wu, Benoit Boulet. 2022-05-19. Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection. https://arxiv.org/abs/2205.09884

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