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.
翻译:时间序列异常现象探测被公认为对真实世界系统可靠和有效运作至关重要,许多异常现象探测方法是根据关于异常特征的各种假设而开发的,但是,由于真实世界数据的复杂性,一个时间序列中不同的异常现象通常具有不同的特征,支持不同的异常假设。这使得很难找到一个能够一贯优于其他模型的单一异常现象探测器。在这项工作中,为了利用不同基础模型的效益,我们提议了一个强化学习模型选择框架。具体地说,我们首先学习了不同的异常现象检测模型,然后利用强化学习,从这些基本模型中动态地选择了候选模型。对真实世界数据的实验表明,拟议的战略确实能够从总体绩效上超越所有基线模型。