mlsanityData to 7 Oct 2026

Time Series Anomaly Detection using Diffusion-based Models

arXiv 2311.01452 ↗First posted 2 Nov 2023cs.LG cs.AIcited by 11

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

Diffusion models have been recently used for anomaly detection (AD) in images. In this paper we investigate whether they can also be leveraged for AD on multivariate time series (MTS). We test two diffusion-based models and compare them to several strong neural baselines. We also extend the PA%K protocol, by computing a ROCK-AUC metric, which is agnostic to both the detection threshold and the ratio K of correctly detected points. Our models outperform the baselines on synthetic datasets and are competitive on real-world datasets, illustrating the potential of diffusion-based methods for AD in multivariate time series.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 33 of its 60 references that are in the map. First posted on arXiv 2 Nov 2023.

Builds on

33 of 60 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection3 Jul
  2. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting28 Jan

Other references · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  7. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  8. 2022Generative Adversarial Networkscited by 5,603
  9. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  10. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  11. 2022Video Diffusion Modelscited by 2,280
  12. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
19 more
  1. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
  2. 2022Transformers in Time Series: A Surveycited by 489
  3. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
  4. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  5. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 300
  6. 2022Diffsound: Discrete Diffusion Model for Text-to-sound Generationcited by 256
  7. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 223
  8. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  9. 2020A review on outlier/anomaly detection in time series datacited by 187
  10. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 181
  11. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  12. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 165
  13. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  14. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  15. 2021TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learningcited by 116
  16. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 95
  17. 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 55
  18. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  19. 2021Sentence Bottleneck Autoencoders from Transformer Language Modelscited by 19

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 6; 2026: 1.

11 papers.

  1. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 66
  2. 2023Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solutioncited by 12
  3. 2024Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoodscited by 10
  4. 2025Anomaly Detection and Generation with Diffusion Models: A Surveycited by 8
  5. 2025Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Seriescited by 2
  6. 2025Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Modelscited by 1
  7. 2024Fault Detection in Mobile Networks Using Diffusion Modelscited by 0
  8. 2025ChronoGraph: A Real-World Graph-Based Multivariate Time Series Datasetcited by 0
  9. 2025AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Usecited by 0
  10. 2025Intention-aware Hierarchical Diffusion Model for Long-term Trajectory Anomaly Detectioncited by 0
  11. 2026Selective Denoising Diffusion Model for Time Series Anomaly Detectioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Prediction
  2. 2024Anomaly detection using Diffusion-based methods
  3. 2025Multivariate Time Series Anomaly Detection using DiffGAN Model
  4. 2025Research on Anomaly Detection Methods Based on Diffusion Models
  5. 2023On Diffusion Modeling for Anomaly Detection
  6. 2024Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection
  7. 2025A Survey on Diffusion Models for Anomaly Detection
  8. 2023Diffusion Models for Time Series Applications: A Survey