重返修复：面向时序异常检测的极简去噪网络

摘要

我们提出JuRe（仅修复）——一种用于时间序列异常检测的极简去噪网络，其核心发现表明：当训练目标正确实现流形投影原则时，复杂的网络架构并非必要。JuRe仅包含一个隐藏维度为128的深度可分离卷积残差块，通过修复被破坏的时间序列窗口进行训练，并在推理时采用无参数的固定结构差异函数进行评分。尽管未使用注意力机制、潜变量或对抗组件，该模型在TSB-AD多变量基准测试中（AUC-PR 0.404，180个序列，17个数据集）位列第二，在UCR单变量档案库的AUC-PR指标上（0.198，250个序列）同样排名第二，且在AUC-PR和VUS-PR指标上领先所有神经网络基线。TSB-AD上的组件消融实验表明训练时引入数据破坏是主导因素（移除该组件导致ΔAUC-PR=0.047），证实驱动检测质量的是去噪目标而非网络容量。基于TSB-AD的成对Wilcoxon符号秩检验显示，在25个基线方法中有21个与JuRe存在统计显著性差异。代码已发布于https://github.com/iis-esslingen/JuRe。

English

We introduce JuRe (Just Repair), a minimal denoising network for time series anomaly detection that exposes a central finding: architectural complexity is unnecessary when the training objective correctly implements the manifold-projection principle. JuRe consists of a single depthwise-separable convolutional residual block with hidden dimension 128, trained to repair corrupted time series windows and scored at inference by a fixed, parameter-free structural discrepancy function. Despite using no attention, no latent variable, and no adversarial component, JuRe ranks second on the TSB-AD multivariate benchmark (AUC-PR 0.404, 180 series, 17 datasets) and second on the UCR univariate archive by AUC-PR (0.198, 250 series), leading all neural baselines on AUC-PR and VUS-PR. Component ablation on TSB-AD identifies training-time corruption as the dominant factor (ΔAUC-PR = 0.047 on removal), confirming that the denoising objective, not network capacity, drives detection quality. Pairwise Wilcoxon signed-rank tests establish statistical significance against 21 of 25 baselines on TSB-AD. Code is available at the URL https://github.com/iis-esslingen/JuRe.

重返修复：面向时序异常检测的极简去噪网络

Back to Repair: A Minimal Denoising Network\ for Time Series Anomaly Detection

摘要

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