论文标题
时间序列通过增强学习模型选择的异常检测
Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection
论文作者
论文摘要
时间序列异常检测已被认为对于现实世界系统的可靠和有效运行至关重要。基于对异常特征的各种假设，已经开发了许多异常检测方法。但是，由于现实世界数据的复杂性质，时间序列中的不同异常通常具有支持不同异常假设的各种轮廓。这使得很难找到一个可以始终如一以超过其他模型的异常检测器。在这项工作中，为了利用不同基本模型的好处，我们提出了一个基于增强学习的模型选择框架。具体而言，我们首先学习了不同异常检测模型的池，然后利用强化学习从这些基本模型中动态选择候选模型。关于现实世界数据的实验表明，就整体绩效而言，提出的策略确实可以超过所有基线模型。
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.
