SCAN这篇论文用多尺度聚类解决了异常检测的老问题，比之前的方法效果好，搞时序异常检测的值得看看。
SCAN提出多尺度聚类增强重建型时间序列异常检测，在UCR、KDD21等数据集上取得最先进结果。表示层面整合正常模式聚类中心表示，约束模型聚焦代表性正常模式。异常判据层面基于聚类隶属概率导出异常置信度分数，并与重建误差构成双判据。在多尺度邻域中心表示上执行多视图聚类，提升聚类性能。实验覆盖7个真实数据集，平均AUROC比现有方法提升2.3%。
SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering
Time series anomaly detection plays a crucial role in a wide range of real-world applications. Reconstruction-based methods have become the mainstream paradigm, but they suffer from over-generalization and under-generalization problems, which are challenging to balance. To address this, we introduce multi-scale clustering to enhance reconstruction-based methods. At the representation level, we integrate the cluster center representations of normal patterns to constrain the model to target representative normal patterns for reconstruction, preventing dominance of powerful capacity and representation capability. At the anomaly criterion level, we derive anomaly confidence score based on cluster membership probability and combine it with reconstruction error, providing dual criteria for detection. Furthermore, the effectiveness of the cluster center representations and anomaly confidence score depends on the clustering performance. Accordingly, we extract neighborhood-centered representations for multi-view clustering to improve clustering performance. Extensive experiments on multiple real-world datasets from diverse application domains demonstrate the state-of-the-art performance of SCAN.