mlsanityData to 7 Oct 2026

Neural Contextual Anomaly Detection for Time Series

arXiv 2107.07702 ↗First posted 16 Jul 2021cs.LG cs.AIcited by 42

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Abstract (arXiv)

We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly detection on time series that scales seamlessly from the unsupervised to supervised setting, and is applicable to both univariate and multivariate time series. This is achieved by effectively combining recent developments in representation learning for multivariate time series, with techniques for deep anomaly detection originally developed for computer vision that we tailor to the time series setting. Our window-based approach facilitates learning the boundary between normal and anomalous classes by injecting generic synthetic anomalies into the available data. Moreover, our method can effectively take advantage of all the available information, be it as domain knowledge, or as training labels in the semi-supervised setting. We demonstrate empirically on standard benchmark datasets that our approach obtains a state-of-the-art performance in these settings.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Builds on 26 of its 54 references that are in the map. First posted on arXiv 16 Jul 2021.

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26 of 54 references.

References · 26

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  4. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,447
  5. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  6. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  7. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 423
  8. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  9. 2019Deep Semi-Supervised Anomaly Detectioncited by 290
  10. 2019Unsupervised Scalable Representation Learning for Multivariate Time Seriescited by 267
  11. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  12. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
14 more
  1. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  2. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  3. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 165
  4. 2019Time-Series Anomaly Detection Service at Microsoftcited by 163
  5. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 148
  6. 2020Explainable Deep One-Class Classificationcited by 126
  7. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 87
  8. 2018Precision and Recall for Time Seriescited by 67
  9. 2020Rethinking Assumptions in Deep Anomaly Detectioncited by 45
  10. 2017Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic datacited by 12
  11. 2020Anomaly Detection at Scale: The Case for Deep Distributional Time Series Modelscited by 7
  12. 2021Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Seriescited by 4
  13. 2020Deep Anomaly Detection by Residual Adaptationcited by 2
  14. 2019Learning Ensembles of Anomaly Detectors on Synthetic Datacited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 12; 2023: 8; 2024: 9; 2025: 6; 2026: 6.

42 papers.

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  1. 2024Chronos: Learning the Language of Time Seriescited by 712
  2. 2023Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospectscited by 97
  3. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
  4. 2024Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signalscited by 29
  5. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detectioncited by 26
  6. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detectioncited by 26
  7. 2022Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlookcited by 24
  8. 2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 24
  9. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 22
  10. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  11. 2022Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detectioncited by 16
  12. 2022Detecting Anomalies within Time Series using Local Neural Transformationscited by 12
30 more
  1. 2023Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solutioncited by 12
  2. 2024PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detectioncited by 12
  3. 2023RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervisioncited by 11
  4. 2022Diverse Counterfactual Explanations for Anomaly Detection in Time Seriescited by 10
  5. 2023Precursor-of-Anomaly Detection for Irregular Time Seriescited by 9
  6. 2025Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomaliescited by 7
  7. 2023Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodologycited by 6
  8. 2024SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detectioncited by 6
  9. 2022Online Time Series Anomaly Detection with State Space Gaussian Processescited by 4
  10. 2022Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detectioncited by 4
  11. 2022Graph similarity learning for change-point detection in dynamic networkscited by 4
  12. 2022TreeDRNet:A Robust Deep Model for Long Term Time Series Forecastingcited by 4
  13. 2025A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challengescited by 4
  14. 2025GenIAS: Generator for Instantiating Anomalies in time Seriescited by 4
  15. 2025RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Datacited by 4
  16. 2022Deep Federated Anomaly Detection for Multivariate Time Series Datacited by 3
  17. 2023Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Modelscited by 3
  18. 2023Open-Set Multivariate Time-Series Anomaly Detectioncited by 3
  19. 2025Noise-Resilient Point-wise Anomaly Detection in Time Series Using Weak Segment Labelscited by 3
  20. 2022Intrinsic Anomaly Detection for Multi-Variate Time Seriescited by 2
  21. 2021Monte Carlo EM for Deep Time Series Anomaly Detectioncited by 1
  22. 2024Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly Detectioncited by 1
  23. 2024Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detectioncited by 1
  24. 2025CAPMix: Robust KPI Anomaly Detection for AIOps in Noisy and Dynamic Environmentscited by 0
  25. 2026IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detectioncited by 0
  26. 2026Learning Unified Representations of Normalcy for Time Series Anomaly Detectioncited by 0
  27. 2026Multi-Modal Anomaly Detection: A Surveycited by 0
  28. 2026CAST: Context- and Anomaly Structure-Conditioned Time Series Anomaly Generationcited by 0
  29. 2026Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transportcited by 0
  30. 2026Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairingcited by 0