mlsanityData to 7 Oct 2026

Just Repair: A Minimal Denoising Network for Time Series Anomaly Detection

arXiv 2604.17388 ↗First posted 19 Apr 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Time series anomaly detectors have grown steadily more complex, incorporating attention mechanisms, adversarial training, and stochastic latent variables. Yet, it is unclear how much of this machinery detection actually requires. We test this question with JuRe (Just Repair), a deliberately minimal detector: a single depthwise-separable convolutional residual block trained to repair Gaussian-corrupted, channel-masked windows, scored at inference by a fixed structural discrepancy function with no learned parameters. JuRe ranks second on the TSB-AD multivariate benchmark (AUC-PR 0.404 over 180 series) and second on the UCR univariate archive (AUC-PR 0.201 over 250 series), where it leads all neural baselines. On TSB-AD, JuRe runs roughly 20× faster than AxonAD, one of the top-ranked methods on that benchmark. Full-benchmark ablations show that removing Gaussian corruption reduces AUC-PR by 0.046, whereas AUC-PR across the evaluated architecture variants spans at most 0.017. A synthetic linear-manifold experiment provides partial evidence for this geometric interpretation: anomaly scores correlate with true off-manifold distance (Pearson r=0.725), and repair directions align increasingly with the true projection as anomaly magnitude grows. Wilcoxon signed-rank tests with Holm correction find significant differences against 20 of 25 baselines, although dependence among series limits dataset-level interpretation. Code is available at https://github.com/iis-esslingen/JuRe.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 38 references that are in the map. First posted on arXiv 19 Apr 2026.

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18 of 38 references.

References · 18

  1. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  2. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  3. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  4. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  5. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  6. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 577
  7. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  8. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  9. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  10. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  11. 2023FITS: Modeling Time Series with 10k Parameterscited by 185
  12. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
6 more
  1. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 148
  2. 2020COPOD: Copula-Based Outlier Detectioncited by 143
  3. 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
  4. 2023When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detectioncited by 17
  5. 2025STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detectioncited by 2
  6. 2026Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detectioncited by 1

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