mlsanityData to 14 Oct 2026

NeuCoReClass AD: Redefining Self-Supervised Time Series Anomaly Detection

arXiv 2508.00909 ↗First posted 29 Jul 2025cs.LGcited by 1

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Abstract (arXiv)

Time series anomaly detection plays a critical role in a wide range of real-world applications. Among unsupervised approaches, self-supervised learning has gained traction for modeling normal behavior without the need of labeled data. However, many existing methods rely on a single proxy task, limiting their ability to capture meaningful patterns in normal data. Moreover, they often depend on handcrafted transformations tailored specific domains, hindering their generalization accross diverse problems. To address these limitations, we introduce NeuCoReClass AD, a self-supervised multi-task time series anomaly detection framework that combines contrastive, reconstruction, and classification proxy tasks. Our method employs neural transformation learning to generate augmented views that are informative, diverse, and coherent, without requiring domain-specific knowledge. We evaluate NeuCoReClass AD across a wide range of benchmarks, demonstrating that it consistently outperforms both classical baselines and most deep-learning alternatives. Furthermore, it enables the characterization of distinct anomaly profiles in a fully unsupervised manner.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 45 references that are in the map. First posted on arXiv 29 Jul 2025.

Builds on

20 of 45 references.

References · 20

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8 more
  1. 2022Detecting Anomalies within Time Series using Local Neural Transformationscited by 12
  2. 2022MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Seriescited by 12
  3. 2022Task-oriented Self-supervised Learning for Anomaly Detection in Electroencephalographycited by 11
  4. 2022Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Successcited by 11
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterizationcited by 0