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

@inproceedings{aaai2018_attend_102368,
  title = {{Attend and Diagnose: Clinical Time Series Analysis Using Attention Models}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11635},
}

@inproceedings{iclr2023_timesnet_33055,
  title = {{TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11976},
}

@inproceedings{cvpr2018_mx_13353,
  title = {{MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2018},
  url = {https://openaccess.thecvf.com/content_cvpr_2018/html/Hasan_MX-LSTM_Mixing_Tracklets_CVPR_2018_paper.html},
}

@inproceedings{neurips2022_film_66506,
  title = {{FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/55013},
}

@inproceedings{iclr2024_timemixer_33728,
  title = {{TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19347},
}

@inproceedings{aaai2025_wpmixer_11262,
  title = {{WPMixer: Efficient Multi-Resolution Mixing 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/34156},
}

@inproceedings{aaai2024_hdmixer_6175,
  title = {{HDMixer: Hierarchical Dependency with Extendable Patch for Multivariate Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29155},
}

@inproceedings{aaai2025_unlocking_11174,
  title = {{Unlocking the Power of LSTM 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/33303},
}

@inproceedings{aaai2017_nonlinear_104429,
  title = {{Nonlinear Dynamic Boltzmann Machines for Time-Series Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10806},
}

@inproceedings{aaai2020_tensorized_100595,
  title = {{Tensorized LSTM with Adaptive Shared Memory for Learning Trends in Multivariate Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5496},
}

@inproceedings{aaai2021_fully_638,
  title = {{Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/00582-fully-exploiting-cascade-graphs-for-real-time-forwarding-prediction/},
}

@inproceedings{neurips2024_from_71750,
  title = {{From Similarity to Superiority: Channel Clustering for Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95539},
}

@inproceedings{neurips2024_cyclenet_73032,
  title = {{CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94391},
}

@inproceedings{neurips2023_largest_69326,
  title = {{LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73480},
}

@inproceedings{aaai2021_deep_398,
  title = {{Deep Switching Auto-Regressive Factorization: Application to Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/07394-deep-switching-auto-regressive-factorization-application-to-time-series-forecasting/},
}

@inproceedings{aaai2024_learning_6497,
  title = {{Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27768},
}

@inproceedings{neurips2019_shallow_58981,
  title = {{Shallow RNN:  Accurate Time-series Classification on Resource Constrained Devices}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14238},
}

@inproceedings{aaai2022_learning_2339,
  title = {{Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04495-learning-and-dynamical-models-for-sub-seasonal-climate-forecasting-comparison-and-collaboration/},
}

@inproceedings{aaai2016_uncertainty_105099,
  title = {{Uncertainty Propagation in Long-Term Structured Regression on Evolving Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10271},
}

@inproceedings{aaai2025_times2d_11006,
  title = {{Times2D: Multi-Period Decomposition and Derivative Mapping for General Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34164},
}

@inproceedings{icml2024_sparsetsf_43461,
  title = {{SparseTSF: Modeling Long-term Time Series Forecasting with *1k* Parameters}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34991},
}

@inproceedings{neurips2024_chimera_73827,
  title = {{Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93680},
}

@inproceedings{neurips2024_pgn_74597,
  title = {{PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/92992},
}

@inproceedings{neurips2024_attractor_73216,
  title = {{Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94220},
}

@inproceedings{aaai2026_towards_107233,
  title = {{Towards Non-Stationary Time Series Forecasting with Temporal Stabilization and Frequency Differencing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39585},
}

@inproceedings{aaai2025_waveletmixer_11275,
  title = {{WaveletMixer: A Multi-Resolution Wavelets Based MLP-Mixer for Multivariate Long-Term Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34434},
}

@inproceedings{aaai2025_disentangling_8764,
  title = {{Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33717},
}

@inproceedings{iclr2024_self_35205,
  title = {{Self-Supervised Contrastive Learning for Long-term Forecasting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17868},
}

@inproceedings{icml2025_timebridge_48648,
  title = {{TimeBridge: Non-Stationarity Matters for Long-term Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43973},
}

@inproceedings{iclr2025_timemixer_80544,
  title = {{TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/31219},
}

@inproceedings{aaai2026_xlinear_106814,
  title = {{XLinear: A Lightweight and Accurate MLP-Based Model for Long-Term Time Series Forecasting with Exogenous Inputs}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39121},
}

@inproceedings{aaai2025_efficiently_8926,
  title = {{Efficiently Enhancing Long-term Series Forecasting via Ultra-long Lookback Windows}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35386},
}