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

• URL: http://arxiv.org/abs/2604.17388v2
• Date: Mon, 27 Apr 2026 05:40:15 GMT
• Title: Back to Repair: A Minimal Denoising Network for Time Series Anomaly Detection
• Abstract summary: JuRe is a minimal denoising network for time series anomaly detection.<n>It is trained to repair corrupted time series windows and scored at inference by a fixed, parameter-free structural discrepancy function.
• Score: 1.7751300245073598
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 ($Δ$AUC-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.

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