Given the scarcity of anomalies in real-world applications, the majority of
literature has been focusing on modeling normality. The learned representations
enable anomaly detection as the normality model is trained to capture certain
key underlying data regularities under normal circumstances. In practical
settings, particularly industrial time series anomaly detection, we often
encounter situations where a large amount of normal operation data is available
along with a small number of anomaly events collected over time. This practical
situation calls for methodologies to leverage these small number of anomaly
events to create a better anomaly detector. In this paper, we introduce two
methodologies to address the needs of this practical situation and compared
them with recently developed state of the art techniques. Our proposed methods
anchor on representative learning of normal operation with autoregressive (AR)
model along with loss components to encourage representations that separate
normal versus few positive examples. We applied the proposed methods to two
industrial anomaly detection datasets and demonstrated effective performance in
comparison with approaches from literature. Our study also points out
additional challenges with adopting such methods in practical applications.

本文介绍了两种方法来解决实际应用中时间序列异常检测的需要，并结合自回归 (AR) 模型进行代表性学习、鼓励区分常态和少量正样本的表征的损失函数分量，将所提出的方法应用于两个工业异常检测数据集，并与文献中的方法进行了比较。此外，本研究还指出了采用此类方法在实际应用中所面临的其他挑战。