Unsupervisedly detecting anomaly points in time series is challenging, which requires the model to learn informative representations and derive a distinguishable criterion. Prior methods mainly detect anomalies based on the recurrent network representation of each time point. However, the point-wise representation is less informative for complex temporal patterns and can be dominated by normal patterns, making rare anomalies less distinguishable. We find that in each time series, each time point can also be described by its associations with all time points, presenting as a point-wise distribution that is more expressive for temporal modeling. We further observe that due to the rarity of anomalies, it is harder for anomalies to build strong associations with the whole series and their associations shall mainly concentrate on the adjacent time points. This observation implies an inherently distinguishable criterion between normal and abnormal points, which we highlight as the \emph{Association Discrepancy}. Technically we propose the \emph{Anomaly Transformer} with an \emph{Anomaly-Attention} mechanism to compute the association discrepancy. A minimax strategy is devised to amplify the normal-abnormal distinguishability of the association discrepancy. Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks for three applications: service monitoring, space \& earth exploration, and water treatment.

本研究旨在通过使用Transformer模型中的Self-Attention机制来解决无监督时间序列数据中异常点检测问题，策略是基于异常点的相邻时间点建立关联，使其排除与整个时间序列的关系，通过计算不同点的关联度来判断是否为异常点，并在六个应用领域中取得了最佳表现。

异常转换器：基于联合偏差的时间序列异常检测