Back to Repair: A Minimal Denoising Network\ for Time Series Anomaly Detection
Summary
This paper introduces JuRe (Just Repair), a minimal denoising network for time series anomaly detection that matches or exceeds complex neural baselines on the TSB-AD and UCR benchmarks, demonstrating that a proper manifold-projection training objective is more important than architectural complexity.
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Paper page - Back to Repair: A Minimal Denoising Network\ for Time Series Anomaly Detection
Source: https://huggingface.co/papers/2604.17388
Abstract
JuRe, a simple denoising network for time series anomaly detection, demonstrates that architectural simplicity can match or exceed complex models when the training objective properly implements the manifold-projection principle.
We introduce JuRe (Just Repair), a minimaldenoising networkfortime series anomaly detectionthat exposes a central finding: architectural complexity is unnecessary when the training objective correctly implements themanifold-projection principle. JuRe consists of a singledepthwise-separable convolutional residual blockwith hidden dimension 128, trained to repair corrupted time series windows and scored at inference by a fixed, parameter-freestructural discrepancy function. Despite using no attention, no latent variable, and no adversarial component, JuRe ranks second on the TSB-AD multivariate benchmark (AUC-PR0.404, 180 series, 17 datasets) and second on the UCR univariate archive byAUC-PR(0.198, 250 series), leading all neural baselines onAUC-PRandVUS-PR. Component ablation on TSB-AD identifiestraining-time corruptionas the dominant factor (ΔAUC-PR= 0.047 on removal), confirming that the denoising objective, not network capacity, drives detection quality.Pairwise Wilcoxon signed-rank testsestablish 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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