mlsanityData to 14 Oct 2026

TSGM: Regular and Irregular Time-series Generation using Score-based Generative Models

arXiv 2511.21335 ↗First posted 26 Nov 2025cs.LGcited by 3

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Score-based generative models (SGMs) have demonstrated unparalleled sampling quality and diversity in numerous fields, such as image generation, voice synthesis, and tabular data synthesis, etc. Inspired by those outstanding results, we apply SGMs to synthesize time-series by learning its conditional score function. To this end, we present a conditional score network for time-series synthesis, deriving a denoising score matching loss tailored for our purposes. In particular, our presented denoising score matching loss is the conditional denoising score matching loss for time-series synthesis. In addition, our framework is such flexible that both regular and irregular time-series can be synthesized with minimal changes to our model design. Finally, we obtain exceptional synthesis performance on various time-series datasets, achieving state-of-the-art sampling diversity and quality.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 32 of its 50 references that are in the map. First posted on arXiv 26 Nov 2025.

Builds on

32 of 50 references.

Closest ancestors · 6

Same direction, newest first

  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applications2 Sep
  2. 2022SOS: Score-based Oversampling for Tabular Data17 Jun
  3. 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANs15 Dec
  4. 2021Gotta Go Fast When Generating Data with Score-Based Models28 May
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  6. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 26

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,101
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  5. 2018Neural Ordinary Differential Equationscited by 3,779
  6. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  7. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,304
  8. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  9. 2013Generating Sequences With Recurrent Neural Networkscited by 554
  10. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  11. 2020Neural Controlled Differential Equations for Irregular Time Seriescited by 506
  12. 2019Sliced Score Matching: A Scalable Approach to Density and Score Estimationcited by 384
14 more
  1. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 337
  2. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  3. 2019GRU-ODE-Bayes: Continuous modeling of sporadically-observed time seriescited by 231
  4. 2018Adversarial Audio Synthesiscited by 225
  5. 2016Professor Forcing: A New Algorithm for Training Recurrent Networkscited by 216
  6. 2024Diffusion-TS: Interpretable Diffusion for General Time Series Generationcited by 184
  7. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 142
  8. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  9. 2020COT-GAN: Generating Sequential Data via Causal Optimal Transportcited by 71
  10. 2023Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEscited by 49
  11. 2022GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networkscited by 44
  12. 2020Modeling Continuous Stochastic Processes with Dynamic Normalizing Flowscited by 37
  13. 2020Smoothness and Stability in GANscited by 27
  14. 2020Autoregressive Score Matchingcited by 15

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observationscited by 2
  2. 2026Diffusion Models for Adaptive Sequential Data Generationcited by 1
  3. 2026Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Regular Time-series Generation using SGM
  2. 2021On tuning consistent annealed sampling for denoising score matching
  3. 2022Convergence of score-based generative modeling for general data distributions
  4. 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach
  5. 2023Score Mismatching for Generative Modeling
  6. 2022Wavelet Score-Based Generative Modeling
  7. 2024Score-based Generative Models with Adaptive Momentum
  8. 2024An analysis of the noise schedule for score-based generative models