Paper Detail
Kadir-Kaan Özer, René Ebeling, Markus Enzweiler
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.
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@misc{zer2026back,
title = {Back to Repair: A Minimal Denoising Network\textbackslash{} for Time Series Anomaly Detection},
author = {Kadir-Kaan Özer and René Ebeling and Markus Enzweiler},
year = {2026},
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 n},
url = {https://huggingface.co/papers/2604.17388},
keywords = {denoising network, time series anomaly detection, manifold-projection principle, depthwise-separable convolutional residual block, structural discrepancy function, training-time corruption, AUC-PR, VUS-PR, pairwise Wilcoxon signed-rank tests, code available, huggingface daily},
eprint = {2604.17388},
archiveprefix = {arXiv},
}
{}