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

2026-04-19

AI Summary

This work addresses the overreliance on complex architectures in time series anomaly detection by proposing JuRe (Just Repair), an exceptionally minimalist approach that employs only a single depthwise-separable convolutional residual block to construct a denoising network. During training, JuRe learns to reconstruct corrupted temporal windows, while at inference time it leverages a parameter-free structural discrepancy function for scoring anomalies. Notably, JuRe eschews attention mechanisms, latent variables, and adversarial components, demonstrating for the first time that—under proper manifold projection—the design of the training objective is more decisive than model capacity for performance. On the TSB-AD multivariate benchmark, JuRe achieves an AUC-PR of 0.404 (ranking second), and on the UCR univariate datasets, it attains an AUC-PR of 0.198 (also second), outperforming all neural baselines in both AUC-PR and VUS-PR metrics.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Time-Series/Data Streams
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• time series anomaly detection
• denoising network
• manifold projection
• architectural complexity
• structural discrepancy

Innovation

• denoising network
• time series anomaly detection
• manifold projection
• depthwise-separable convolution
• structural discrepancy