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

@inproceedings{neurips2020_adaptive_59323,
  title = {{Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18209},
}

@inproceedings{iclr2018_diffusion_28606,
  title = {{Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/80},
}

@inproceedings{cvpr2019_memory_14507,
  title = {{Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{aaai2023_spatio_4716,
  title = {{Spatio-Temporal Meta-Graph Learning for Traffic Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25976},
}

@inproceedings{neurips2022_scinet_64232,
  title = {{SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53511},
}

@inproceedings{aaai2021_inductive_780,
  title = {{Inductive Graph Neural Networks for Spatiotemporal Kriging}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/04478-inductive-graph-neural-networks-for-spatiotemporal-kriging/},
}

@inproceedings{aaai2021_fc_580,
  title = {{FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09233-fc-gaga-fully-connected-gated-graph-architecture-for-spatio-temporal-traffic-forecasting/},
}

@inproceedings{aaai2020_time2graph_101909,
  title = {{Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5769},
}

@inproceedings{icml2021_z_40127,
  title = {{Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/9391},
}

@inproceedings{iclr2022_filling_31613,
  title = {{Filling the G\_ap\_s: Multivariate Time Series Imputation by Graph Neural Networks}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/5891},
}

@inproceedings{neurips2018_multivariate_57411,
  title = {{Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11332},
}

@inproceedings{neurips2021_probabilistic_61826,
  title = {{Probabilistic Transformer For Time Series Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26279},
}

@inproceedings{aaai2025_rhythmmamba_10561,
  title = {{RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33204},
}

@inproceedings{neurips2022_learning_65572,
  title = {{Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2022},
  url = {https://nips.cc/virtual/2022/poster/53145},
}

@inproceedings{iclr2023_effectively_31957,
  title = {{Effectively Modeling Time Series with Simple Discrete State Spaces}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/10874},
}

@inproceedings{neurips2021_topological_62458,
  title = {{Topological Attention for Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26763},
}

@inproceedings{aaai2020_deep_100355,
  title = {{Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5484},
}

@inproceedings{iclr2023_liquid_32955,
  title = {{Liquid Structural State-Space Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11537},
}

@inproceedings{neurips2023_sparse_66937,
  title = {{Sparse Graph Learning from Spatiotemporal Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/73924},
}

@inproceedings{neurips2023_enhancing_68634,
  title = {{Enhancing Adaptive History Reserving by Spiking Convolutional Block Attention Module in Recurrent Neural Networks}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71204},
}

@inproceedings{aaai2025_motif_9991,
  title = {{Motif-aware Graph Neural Networks for Networked Time Series Imputation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33241},
}

@inproceedings{icml2024_efficient_44458,
  title = {{Efficient and Effective Time-Series Forecasting with Spiking Neural Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33994},
}

@inproceedings{icml2024_graph_45339,
  title = {{Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33113},
}

@inproceedings{aaai2024_time_7566,
  title = {{Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29051},
}

@inproceedings{cvpr2025_gg_26358,
  title = {{GG-SSMs: Graph-Generating State Space Models}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2025},
  url = {https://cvpr.thecvf.com/virtual/2025/poster/33961},
}

@inproceedings{icml2024_graph_45629,
  title = {{Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32823},
}

@inproceedings{icml2025_timedart_48922,
  title = {{TimeDART: A Diffusion Autoregressive Transformer for Self-Supervised Time Series Representation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43701},
}

@inproceedings{neurips2025_non_74736,
  title = {{Non-stationary Equivariant Graph Neural Networks for Physical Dynamics Simulation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=0dp90Bq8r8},
}

@inproceedings{aaai2026_medspaformer_107649,
  title = {{MedSpaformer: A Transferable Transformer with Multi-Granularity Token Sparsification for Medical Time Series Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40001},
}

@inproceedings{icml2026_dynamic_53089,
  title = {{Dynamic Relational Priming Improves Transformer in Multivariate Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=bz66grGgjI},
}

@inproceedings{aaai2026_dynamic_107165,
  title = {{Dynamic Semantic Tokenization for Time Series via Elastic Sampling on Physics-aware Perception}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39517},
}

@inproceedings{aaai2026_freqcycle_107690,
  title = {{FreqCycle: A Multi-Scale Time-Frequency Analysis Method for Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40042},
}

@inproceedings{icml2025_understanding_46126,
  title = {{Understanding the Limits of Deep Tabular Methods with Temporal Shift}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46434},
}

@inproceedings{icml2025_channel_47222,
  title = {{Channel Normalization for Time Series Channel Identification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45365},
}

@inproceedings{icml2025_efficient_47664,
  title = {{Efficient Time Series Processing for Transformers and State-Space Models through Token Merging}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44933},
}

@inproceedings{icml2025_cmos_48049,
  title = {{CMoS: Rethinking Time Series Prediction Through the Lens of Chunk-wise Spatial Correlations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44558},
}

@inproceedings{icml2025_rethinking_48638,
  title = {{Rethinking Time Encoding via Learnable Transformation Functions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43983},
}

@inproceedings{icml2026_helix_50752,
  title = {{HELIX: Hybrid Encoding with Learnable Identity and Cross-dimensional Synthesis for Time Series Imputation}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=FN20iuPnEU},
}

@inproceedings{icml2026_medmamba_54583,
  title = {{MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=qPqJH0heR0},
}