% Key papers for Diffusion models for time series (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2024_latent_6426,
  title = {{Latent Diffusion Transformer for Probabilistic Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29085},
}

@inproceedings{icml2021_autoregressive_39073,
  title = {{Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/8591},
}

@inproceedings{neurips2022_generative_66123,
  title = {{Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53118},
}

@inproceedings{aaai2025_auto_8188,
  title = {{Auto-Regressive Moving Diffusion Models for Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33838},
}

@inproceedings{neurips2020_modeling_60342,
  title = {{Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/16957},
}

@inproceedings{neurips2023_predict_69547,
  title = {{Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70377},
}

@inproceedings{neurips2024_retrieval_73089,
  title = {{Retrieval-Augmented Diffusion Models for Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94339},
}

@inproceedings{icml2023_non_43160,
  title = {{Non-autoregressive Conditional Diffusion Models for Time Series Prediction}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/25084},
}

@inproceedings{iclr2024_diffusion_33624,
  title = {{Diffusion-TS: Interpretable Diffusion for General Time Series Generation}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19451},
}

@inproceedings{neurips2024_fide_70513,
  title = {{FIDE: Frequency-Inflated Conditional Diffusion Model for Extreme-Aware Time Series Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96595},
}

@inproceedings{icml2023_modeling_42187,
  title = {{Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2023},
  url = {https://icml.cc/virtual/2023/poster/23676},
}

@inproceedings{aaai2025_population_10326,
  title = {{Population Aware Diffusion for Time Series Generation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34038},
}

@inproceedings{neurips2024_ant_70237,
  title = {{ANT: Adaptive Noise Schedule for Time Series Diffusion Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96850},
}

@inproceedings{cvpr2022_deep_18609,
  title = {{Deep Decomposition for Stochastic Normal-Abnormal Transport}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2022},
}

@inproceedings{neurips2024_sdformer_72749,
  title = {{SDformer: Similarity-driven Discrete Transformer For Time Series Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94642},
}

@inproceedings{icml2024_time_44566,
  title = {{Time Series Diffusion in the Frequency Domain}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33886},
}

@inproceedings{neurips2025_a_76700,
  title = {{A Diffusion Model for Regular Time Series Generation from Irregular Data with Completion and Masking}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=M9JmlA6Cgf},
}

@inproceedings{iclr2025_sigdiffusions_82525,
  title = {{SigDiffusions: Score-Based Diffusion Models for Time Series via Log-Signature Embeddings}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/32077},
}

@inproceedings{iclr2025_continuous_82872,
  title = {{Continuous Ensemble Weather Forecasting with Diffusion models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28928},
}

@inproceedings{aaai2026_dim_106342,
  title = {{DiM-TS: Bridge the Gap Between Selective State Space Models and Time Series for Generative Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/38649},
}

@inproceedings{aaai2026_ecodiffusion_108881,
  title = {{EcoDiffusion: Uncertainty-Aware Emulation of Ecosystem Processes with Conditional Diffusion for Long Sequences with Single-Step Initialization}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/41233},
}

@inproceedings{iclr2024_mg_33906,
  title = {{MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19169},
}

@inproceedings{icml2025_non_47817,
  title = {{Non-stationary Diffusion For Probabilistic Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44783},
}

@inproceedings{icml2026_diff_54211,
  title = {{Diff-MN: Diffusion Parameterized MoE-NCDE for Continuous Time Series Generation with Irregular Observations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=mx8nvMUfxL},
}

@inproceedings{icml2026_latent_54832,
  title = {{Latent Laplace Diffusion for Irregular Multivariate Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=t73XUJvyQr},
}

@inproceedings{neurips2025_diffusion_74776,
  title = {{Diffusion Transformers for Imputation: Statistical Efficiency and Uncertainty Quantification}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=15mlgnyaFt},
}

@inproceedings{neurips2025_single_76911,
  title = {{Single-Step Operator Learning for Conditioned Time-Series Diffusion Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=ONUZ08OAZL},
}