Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables effective discrimination of anomalies. Reconstruction-based methods still dominate, but the representation learning with anomalies might hurt the performance with its large abnormal loss. On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection. In this paper, we propose DCdetector, a multi-scale dual attention contrastive representation learning model. DCdetector utilizes a novel dual attention asymmetric design to create the permutated environment and pure contrastive loss to guide the learning process, thus learning a permutation invariant representation with superior discrimination abilities. Extensive experiments show that DCdetector achieves state-of-the-art results on multiple time series anomaly detection benchmark datasets. Code is publicly available at https://github.com/DAMO-DI-ML/KDD2023-DCdetector.

翻译：时间序列异常检测在众多应用中至关重要，其目标是从时间序列的正常样本分布中识别出异常样本。该任务最根本的挑战在于学习一种能够有效区分异常的表征映射。基于重构的方法仍占主导地位，但异常参与下的表征学习可能因较大的异常损失而损害性能。另一方面，对比学习旨在找到能够清晰区分任意实例与其他实例的表征，这为时间序列异常检测提供了更自然且更具前景的表征方法。本文提出DCdetector——一种多尺度双注意力对比表示学习模型。DCdetector采用新颖的双注意力非对称设计来构建排列环境，并通过纯对比损失引导学习过程，从而学习到具有卓越判别能力的排列不变表征。广泛实验表明，DCdetector在多个时间序列异常检测基准数据集上取得了最先进的结果。代码已开源至：https://github.com/DAMO-DI-ML/KDD2023-DCdetector。