提出PAI方法，让时序异常检测更关注信号幅度信息，显著提升检测效果。
PAI: Preserving Amplitude Information in Representation-Based Time-Series Anomaly Detection
- 设计诊断模块与分数增强函数，主动保留嵌入表示中的幅度信息。
- 在两个数据集上平均提升VUS-PR达98.4%和36.8%，最佳组合超越当前最优15%。
- 适用于对幅度变化敏感的异常检测场景，如工业传感器、金融时序等。
基于表示的时序异常检测方法在多种任务中表现优异，但我们发现其普遍存在一个关键缺陷：学习到的嵌入表示往往对幅度不敏感。丢失幅度信息会降低对幅度相关异常的检测性能，且这一问题在现有所有表示类方法中普遍存在。为此，我们提出一种新的异常评分方案PAI，包含诊断模块和最终分数增强函数。诊断模块通过对比余弦与欧氏距离得分，判断嵌入是否已捕捉幅度信息；随后在分数增强函数中，计算逐点中位数与MAD偏差得分、局部均值偏移得分，并与表示分数融合生成最终异常分数。在TSB-AD-U-Eva和TAB UV数据集上，PAI使四种评估的方法在所有指标上均获得提升，平均VUS-PR分别提高98.4%和36.8%。其中PaAno + PAI达到最佳性能，超越当前最优方法15%。对置信区间、异常类型分解及输入归一化消融实验的进一步验证支持该方案。结果表明，显式保留幅度信息对基于表示的时序异常检测至关重要，而这一点在现有评分方案中被长期忽视。代码已开源。
原文摘要 · Abstract (English)
Representation-based time-series anomaly detection algorithms significantly outperform other methods on diverse anomaly detection tasks. However, we notice that they suffer from a major limitation in our evaluation - their learned embeddings are often amplitude-agnostic. Losing amplitude information can degrade performance on amplitude related anomalies, and this failure is prevalent across all existing representation-based methods. To address aforementioned issues, we propose a new anomaly scoring scheme named PAI. PAI consists of two complementary modules, a diagnostic module and a final score augmentation function. The diagnostic module compares cosine and Euclidean scoring on the same representation bank to test whether amplitude information is already captured in the learned representation. Then in final score augmentation function, PAI computes a point-wise median and MAD deviation score and a local mean-shift score-which are fused with the representation score to produce the final anomaly score. On the TSB-AD-U-Eva and TAB UV datasets, PAI improves all four evaluated representation-based methods across every reported metric, achieving average VUS-PR gains of 98.4% and 36.8%, respectively. Among all evaluated combinations, PaAno + PAI achieves the best performance, outperforming the state-of-the-art method by 15%. Further evaluation on bootstrap confidence intervals, anomaly-type breakdowns, and a TS2Vec input-normalization ablation further support the proposed scheme. These results suggest that explicitly retaining amplitude information is important for representation-based time-series anomaly detection, which has been underemphasized in existing scoring schemes. Code is available at: https://github.com/pantheon5100/PAI
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