重返修復：一種用於時間序列異常檢測的最小去噪網絡

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，其核心发现是：当训练目标正确实现流形投影原则时，网络架构的复杂性并非必要。JuRe仅包含单个隐藏维度为128的深度可分离卷积残差块，通过修复受损时间序列窗口进行训练，并在推理时通过固定的无参数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，JuRe在TSB-AD多元基准测试中（AUC-PR 0.404，180个序列，17个数据集）排名第二，在UCR单变量档案库的AUC-PR指标上（0.198，250个序列）位列第二，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添加噪声是主导因素（移除后ΔAUC-PR=0.047），证实驱动检测质量的是去噪目标而非网络容量。配对Wilcoxon符号秩检验显示，在TSB-AD上JuRe与25个基线中的21个存在统计显著性差异。代码详见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.

重返修復：一種用於時間序列異常檢測的最小去噪網絡

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

摘要

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