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

@inproceedings{neurips2023_one_69017,
  title = {{One Fits All: Power General Time Series Analysis by Pretrained LM}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70856},
}

@inproceedings{aaai2024_gpt4mts_6094,
  title = {{GPT4MTS: Prompt-based Large Language Model for Multimodal Time-series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30383},
}

@inproceedings{aaai2025_timecma_11003,
  title = {{TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34067},
}

@inproceedings{neurips2023_large_69360,
  title = {{Large Language Models Are Zero-Shot Time Series Forecasters}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/70543},
}

@inproceedings{aaai2025_calf_8331,
  title = {{CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34082},
}

@inproceedings{neurips2024_units_73799,
  title = {{UniTS: A Unified Multi-Task Time Series Model}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93709},
}

@inproceedings{neurips2024_are_71137,
  title = {{Are Language Models Actually Useful for Time Series Forecasting?}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96085},
}

@inproceedings{icml2024_a_45164,
  title = {{A decoder-only foundation model for time-series forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33288},
}

@inproceedings{neurips2024_tiny_70349,
  title = {{Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96748},
}

@inproceedings{iclr2024_tempo_34645,
  title = {{TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18429},
}

@inproceedings{neurips2024_autotimes_71270,
  title = {{AutoTimes: Autoregressive Time Series Forecasters via Large Language Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95975},
}

@inproceedings{icml2024_unified_44685,
  title = {{Unified Training of Universal Time Series Forecasting Transformers}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33767},
}

@inproceedings{iclr2024_test_34530,
  title = {{TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18544},
}

@inproceedings{aaai2025_timecap_11001,
  title = {{TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33989},
}

@inproceedings{icml2024_position_45116,
  title = {{Position: What Can Large Language Models Tell Us about Time Series Analysis}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33336},
}

@inproceedings{aaai2025_geolocation_9339,
  title = {{Geolocation Representation from Large Language Models Are Generic Enhancers for Spatio-Temporal Learning}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33879},
}

@inproceedings{neurips2024_from_74238,
  title = {{From News to Forecast: Integrating Event Analysis in LLM-Based Time Series Forecasting with Reflection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93316},
}

@inproceedings{neurips2024_large_74356,
  title = {{Large Pre-trained time series models for cross-domain Time series analysis tasks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93205},
}

@inproceedings{iclr2025_time_82846,
  title = {{Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28954},
}

@inproceedings{icml2025_time_47840,
  title = {{Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44762},
}

@inproceedings{aaai2025_timedp_11004,
  title = {{TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33926},
}

@inproceedings{icml2024_timer_44818,
  title = {{Timer: Generative Pre-trained Transformers Are Large Time Series Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33634},
}