Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection

• URL: http://arxiv.org/abs/2205.09884v2
• Date: Mon, 23 May 2022 14:02:08 GMT
• Title: Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection
• Authors: Jiuqi Elise Zhang, Di Wu, Benoit Boulet
• Abstract summary: Time series anomaly detection is of critical importance for the reliable and efficient operation of real-world systems. In this work, we assume that a pool of anomaly detection models is accessible and propose to utilize reinforcement learning to dynamically select a candidate model. It is demonstrated that the proposed strategy can outperforms all baseline models in terms of overall performance.
• Score: 3.1692938090731584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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