% Key papers for Transformers for long-term forecasting (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2023_are_3122,
  title = {{Are Transformers Effective for Time Series Forecasting?}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26317},
}

@inproceedings{iclr2023_a_32387,
  title = {{A Time Series is Worth 64 Words: Long-term Forecasting with Transformers}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10876},
}

@inproceedings{icml2022_fedformer_40528,
  title = {{FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17985},
}

@inproceedings{neurips2021_autoformer_61879,
  title = {{Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/28217},
}

@inproceedings{neurips2023_frequency_69158,
  title = {{Frequency-domain MLPs are More Effective Learners in Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70726},
}

@inproceedings{aaai2025_unlocking_11175,
  title = {{Unlocking the Power of Patch: Patch-Based MLP for Long-Term Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33378},
}

@inproceedings{cvpr2023_latency_21327,
  title = {{Latency Matters: Real-Time Action Forecasting Transformer}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2023},
  url = {https://cvpr.thecvf.com/virtual/2023/poster/21560},
}

@inproceedings{neurips2024_are_73455,
  title = {{Are Self-Attentions Effective for Time Series Forecasting?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94012},
}

@inproceedings{aaai2023_inparformer_3897,
  title = {{InParformer: Evolutionary Decomposition Transformers with Interactive Parallel Attention for Long-Term Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25845},
}

@inproceedings{aaai2025_affirm_8067,
  title = {{Affirm: Interactive Mamba with Adaptive Fourier Filters for Long-term Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35463},
}

@inproceedings{neurips2024_rethinking_70997,
  title = {{Rethinking Fourier Transform from A Basis Functions Perspective for Long-term Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96209},
}

@inproceedings{aaai2025_hdt_9382,
  title = {{HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/32057},
}

@inproceedings{neurips2024_utilizing_70270,
  title = {{Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96819},
}

@inproceedings{iclr2025_timer_81717,
  title = {{Timer-XL: Long-Context Transformers for Unified Time Series Forecasting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30062},
}

@inproceedings{neurips2024_peri_70538,
  title = {{Peri-midFormer: Periodic Pyramid Transformer for Time Series Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96575},
}

@inproceedings{neurips2024_parsimony_74438,
  title = {{Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93133},
}

@inproceedings{iclr2024_card_34245,
  title = {{CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18830},
}

@inproceedings{aaai2025_sequence_10744,
  title = {{Sequence Complementor: Complementing Transformers for Time Series Forecasting with Learnable Sequences}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33747},
}

@inproceedings{icml2024_samformer_43616,
  title = {{SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34836},
}

@inproceedings{icml2024_loss_43637,
  title = {{Loss Shaping Constraints for Long-Term Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34815},
}

@inproceedings{neurips2025_enhancing_76208,
  title = {{Enhancing the Maximum Effective Window for Long-Term Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=Gmwsy7TlFI},
}

@inproceedings{iclr2026_mixlinear_86492,
  title = {{MixLinear: Extreme Low Resource Multivariate Time Series Forecasting with \$0.1K\$ Parameters}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=QUj0KuCumD},
}

@inproceedings{icml2024_tslanet_43761,
  title = {{TSLANet: Rethinking Transformers for Time Series Representation Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34691},
}

@inproceedings{iclr2026_pmdformer_88847,
  title = {{PMDformer: Patch-Mean Decoupling Transformer for Long-term Forecasting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=rfJ41gK9Ct},
}

@inproceedings{aaai2026_hmformer_107003,
  title = {{HMformer: Unleashing Transformer’s Potential for Time Series Forecasting via Hierarchical Multi-Scale Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39355},
}