mlsanityData to 14 Oct 2026

Regular Time-series Generation using SGM

arXiv 2301.08518 ↗First posted 20 Jan 2023cs.LGcited by 23

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Score-based generative models (SGMs) are generative models that are in the spotlight these days. Time-series frequently occurs in our daily life, e.g., stock data, climate data, and so on. Especially, time-series forecasting and classification are popular research topics in the field of machine learning. SGMs are also known for outperforming other generative models. As a result, we apply SGMs to synthesize time-series data by learning conditional score functions. We propose a conditional score network for the time-series generation domain. Furthermore, we also derive the loss function between the score matching and the denoising score matching in the time-series generation domain. Finally, we achieve state-of-the-art results on real-world datasets in terms of sampling diversity and quality.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 23 of its 40 references that are in the map. First posted on arXiv 20 Jan 2023.

Builds on

23 of 40 references.

Closest ancestors · 5

Same direction, newest first

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

Other references · 18

  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. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
  5. 2021CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputationcited by 582
  6. 2018Deep learning for time series classification: a reviewcited by 576
  7. 2013Generating Sequences With Recurrent Neural Networkscited by 554
  8. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Modelscited by 521
  9. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 337
  10. 2017Real-valued (Medical) Time Series Generation with Recurrent Conditional GANscited by 333
  11. 2018Adversarial Audio Synthesiscited by 225
  12. 2016Professor Forcing: A New Algorithm for Training Recurrent Networkscited by 216
6 more
  1. 2021Symbolic Music Generation with Diffusion Modelscited by 154
  2. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 142
  3. 2016C-RNN-GAN: Continuous recurrent neural networks with adversarial trainingcited by 132
  4. 2020COT-GAN: Generating Sequential Data via Causal Optimal Transportcited by 71
  5. 2021ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Modelscited by 43
  6. 2020Smoothness and Stability in GANscited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 6; 2025: 6; 2026: 5.

23 papers.

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  1. 2023Diffusion Models for Time Series Applications: A Surveycited by 72
  2. 2024A Survey on Diffusion Models for Time Series and Spatio-Temporal Datacited by 67
  3. 2023ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detectioncited by 40
  4. 2023TSGBench: Time Series Generation Benchmarkcited by 26
  5. 2024FlowTS: Time Series Generation via Rectified Flowcited by 22
  6. 2023Quantum-Noise-Driven Generative Diffusion Modelscited by 20
  7. 2024Efficiently Parameterized Neural Metriplectic Systemscited by 19
  8. 2023A Comprehensive Survey on Generative Diffusion Models for Structured Datacited by 8
  9. 2024Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilitiescited by 8
  10. 2025Robust time series generation via Schr"odinger Bridge: a comprehensive evaluationcited by 8
  11. 2025Modeling Human Gaze Behavior with Diffusion Models for Unified Scanpath Predictioncited by 7
  12. 2025TRADES: Generating Realistic Market Simulations with Diffusion Modelscited by 5
11 more
  1. 2024Alternators For Sequence Modelingcited by 3
  2. 2025TimeWak: Temporal Chained-Hashing Watermark for Time Series Datacited by 3
  3. 2025CTBench: Cryptocurrency Time Series Generation Benchmarkcited by 3
  4. 2025WaveletDiff: Multilevel Wavelet Diffusion For Time Series Generationcited by 3
  5. 2023MadSGM: Multivariate Anomaly Detection with Score-based Generative Modelscited by 1
  6. 2024Text2Data: Low-Resource Data Generation with Textual Controlcited by 1
  7. 2026Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Modelscited by 1
  8. 2026Diffusion Models for Adaptive Sequential Data Generationcited by 1
  9. 2026Unconditional flow-based time series generation with equivariance-regularised latent spacescited by 0
  10. 2026High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Frameworkcited by 0
  11. 2026Time series generation with spectrally aligned latent flow matchingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025TSGM: Regular and Irregular Time-series Generation using Score-based Generative Models
  2. 2024A Good Score Does not Lead to A Good Generative Model
  3. 2022Wavelet Score-Based Generative Modeling
  4. 2026Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models
  5. 2021Score-based Generative Modeling in Latent Space
  6. 2023Closed-Form Diffusion Models
  7. 2023Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance
  8. 2025Time Series Generation Under Data Scarcity: A Unified Generative Modeling Approach