mlsanityData to 14 Oct 2026

Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling

arXiv 2311.12550 ↗First posted 21 Nov 2023cs.LG stat.MLcited by 15

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Abstract (arXiv)

We present a novel time series anomaly detection method that achieves excellent detection accuracy while offering a superior level of explainability. Our proposed method, TimeVQVAE-AD, leverages masked generative modeling adapted from the cutting-edge time series generation method known as TimeVQVAE. The prior model is trained on the discrete latent space of a time-frequency domain. Notably, the dimensional semantics of the time-frequency domain are preserved in the latent space, enabling us to compute anomaly scores across different frequency bands, which provides a better insight into the detected anomalies. Additionally, the generative nature of the prior model allows for sampling likely normal states for detected anomalies, enhancing the explainability of the detected anomalies through counterfactuals. Our experimental evaluation on the UCR Time Series Anomaly archive demonstrates that TimeVQVAE-AD significantly surpasses the existing methods in terms of detection accuracy and explainability. We provide our implementation on GitHub: https://github.com/ML4ITS/TimeVQVAE-AnomalyDetection.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 35 of its 61 references that are in the map. First posted on arXiv 21 Nov 2023.

Builds on

35 of 61 references.

References · 35

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2017Neural Discrete Representation Learningcited by 4,712
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  5. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  6. 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,295
  7. 2022MaskGIT: Masked Generative Image Transformercited by 1,099
  8. 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 727
  9. 2021Vector-quantized Image Modeling with Improved VQGANcited by 658
  10. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  11. 2021Time-Series Representation Learning via Temporal and Contextual Contrastingcited by 345
  12. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
23 more
  1. 2020Why Normalizing Flows Fail to Detect Out-of-Distribution Datacited by 221
  2. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  3. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  4. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  5. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  6. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
  7. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 151
  8. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  9. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  10. 2020TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networkscited by 55
  11. 2023Vector Quantized Time Series Generation with a Bidirectional Prior Modelcited by 50
  12. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
  13. 2020Anomaly detection through latent space restoration using vector-quantized variational autoencoderscited by 45
  14. 2022Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Seriescited by 42
  15. 2023TSGBench: Time Series Generation Benchmarkcited by 26
  16. 2021Merlion: A Machine Learning Library for Time Seriescited by 25
  17. 2018Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detectioncited by 18
  18. 2022MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Seriescited by 12
  19. 2021Contrastive Predictive Coding for Anomaly Detectioncited by 10
  20. 2022Diverse Counterfactual Explanations for Anomaly Detection in Time Seriescited by 10
  21. 2022Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriescited by 9
  22. 2023TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networkscited by 3
  23. 2023Masked Generative Modeling with Enhanced Sampling Schemecited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 8.

15 papers.

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  1. 2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzerscited by 20
  2. 2024DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Seriescited by 7
  3. 2026SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Predictioncited by 4
  4. 2025Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAEcited by 3
  5. 2025Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detectioncited by 1
  6. 2026Toward Faithful Explanations in Acoustic Anomaly Detectioncited by 1
  7. 2026ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detectioncited by 1
  8. 2026EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systemscited by 1
  9. 2025VISTA: Unsupervised 2D Temporal Dependency Representations for Time Series Anomaly Detectioncited by 0
  10. 2025Lane-Wise Highway Anomaly Detectioncited by 0
  11. 2025TimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoringcited by 0
  12. 2026COMET: Codebook-based Online-adaptive Multi-scale Embedding for Time-series Anomaly Detectioncited by 0
3 more
  1. 2026ARTA: Adversarial-Robust Multivariate Time--Series Anomaly Detection via Sparsity-Constrained Perturbationscited by 0
  2. 2026Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detectioncited by 0
  3. 2026ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterizationcited by 0