mlsanityData to 14 Oct 2026

Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing

arXiv 2609.33610 ↗First posted 27 Sep 2026cs.AIcited by 0

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Abstract (arXiv)

Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing methods often rely on unsupervised objectives or surrogate abnormal patterns, providing limited supervision for context-dependent normal--anomalous distinctions. We propose Context-Anchored Pair Supervision (CAPS), a supervision-recovery framework for TSAD. CAPS views ideal anomaly supervision as a matched comparison between normal and anomalous outcomes under the same temporal context, and seeks to recover such supervision without target-domain anomaly labels. Using simulated normal--anomalous pairs, CAPS learns structure and anomaly-semantic representations through reconstruction, background consistency, and within-pair counterfactual recombination. The resulting anomaly representations form a continuous semantic space with coarse modes and induce a sampleable multimodal prior. CAPS conditionally realizes sampled semantics as residual-form effects on target reference trajectories. The resulting context-anchored normal--anomalous counterparts provide temporal supervision for discriminative detector learning. Experiments on nine datasets show that CAPS achieves the strongest aggregate performance across all four evaluation metrics among the compared methods, while complementary ablations and transfer analyses support the roles of context anchoring, semantic disentanglement, and conditional realization.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 43 references that are in the map. First posted on arXiv 27 Sep 2026.

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28 of 43 references.

References · 28

  1. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  2. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 471
  3. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  4. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  5. 2019Deep Semi-Supervised Anomaly Detectioncited by 291
  6. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  7. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  8. 2023FITS: Modeling Time Series with 10k Parameterscited by 195
  9. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generationcited by 184
  10. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  11. 2019Deep Anomaly Detection with Deviation Networkscited by 153
  12. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
16 more
  1. 2024ARC: A Generalist Graph Anomaly Detector with In-Context Learningcited by 48
  2. 2021Neural Contextual Anomaly Detection for Time Seriescited by 43
  3. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 38
  4. 2023AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Schemecited by 25
  5. 2024Unseen Visual Anomaly Generationcited by 23
  6. 2024KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networkscited by 23
  7. 2024FlowTS: Time Series Generation via Rectified Flowcited by 22
  8. 2022Detecting Multivariate Time Series Anomalies with Zero Known Labelcited by 21
  9. 2023When Model Meets New Normals: Test-time Adaptation for Unsupervised Time-series Anomaly Detectioncited by 17
  10. 2024Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?cited by 17
  11. 2024LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detectioncited by 15
  12. 2025Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancycited by 10
  13. 2025Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomaliescited by 7
  14. 2026PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detectioncited by 7
  15. 2025Open Challenges in Time Series Anomaly Detection: An Industry Perspectivecited by 5
  16. 2025A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challengescited by 4

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