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

@inproceedings{aaai2024_msgnet_6589,
  title = {{MSGNet: Learning Multi-Scale Inter-series Correlations for Multivariate Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/28991},
}

@inproceedings{aaai2020_data_100267,
  title = {{DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5440},
}

@inproceedings{aaai2020_joint_102194,
  title = {{Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6056},
}

@inproceedings{neurips2020_spectral_60858,
  title = {{Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18660},
}

@inproceedings{aaai2024_fully_6071,
  title = {{Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29500},
}

@inproceedings{iclr2021_discrete_30381,
  title = {{Discrete Graph Structure Learning for Forecasting Multiple Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/2800},
}

@inproceedings{aaai2020_block_102170,
  title = {{Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/6032},
}

@inproceedings{neurips2024_softs_70734,
  title = {{SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96390},
}

@inproceedings{neurips2023_fouriergnn_68680,
  title = {{FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71159},
}

@inproceedings{aaai2020_towards_101906,
  title = {{Towards Better Forecasting by Fusing Near and Distant Future Visions}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2020},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/5766},
}

@inproceedings{aaai2023_svp_4594,
  title = {{SVP-T: A Shape-Level Variable-Position Transformer for Multivariate Time Series Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26359},
}

@inproceedings{aaai2022_catn_1808,
  title = {{CATN: Cross Attentive Tree-Aware Network for Multivariate Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/04030-catn-cross-attentive-tree-aware-network-for-multivariate-time-series-forecasting/},
}

@inproceedings{aaai2023_waveform_4955,
  title = {{WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/26276},
}

@inproceedings{aaai2016_learning_105009,
  title = {{Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10181},
}

@inproceedings{icml2018_hierarchical_36732,
  title = {{Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2018},
  url = {https://icml.cc/virtual/2018/poster/2108},
}

@inproceedings{aaai2025_filterts_9195,
  title = {{FilterTS: Comprehensive Frequency Filtering for Multivariate Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35438},
}

@inproceedings{aaai2025_mptsnet_9869,
  title = {{MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34155},
}

@inproceedings{aaai2023_learning_3999,
  title = {{Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25915},
}

@inproceedings{aaai2024_considering_5472,
  title = {{Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29483},
}

@inproceedings{neurips2024_frequency_72370,
  title = {{Frequency-aware Generative Models for Multivariate Time Series Imputation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94982},
}

@inproceedings{aaai2025_revisiting_10550,
  title = {{Revisiting Attention for Multivariate Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35455},
}

@inproceedings{aaai2025_timecheat_11002,
  title = {{TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series Analysis}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34076},
}

@inproceedings{icml2024_revitalizing_43594,
  title = {{Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34858},
}

@inproceedings{neurips2024_addressing_71192,
  title = {{Addressing Spatial-Temporal Heterogeneity: General Mixed Time Series Analysis via Latent Continuity Recovery and Alignment}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/96039},
}

@inproceedings{aaai2026_fredn_106735,
  title = {{FreDN: Spectral Disentanglement for Time Series Forecasting via Learnable Frequency Decomposition}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39042},
}

@inproceedings{aaai2025_csformer_8367,
  title = {{CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/35406},
}

@inproceedings{icml2024_cats_43315,
  title = {{CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/35137},
}

@inproceedings{aaai2025_vardrop_11228,
  title = {{VarDrop: Enhancing Training Efficiency by Reducing Variate Redundancy in Periodic Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33951},
}

@inproceedings{iclr2024_rethinking_34147,
  title = {{Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18928},
}

@inproceedings{neurips2024_structured_73040,
  title = {{Structured Matrix Basis for Multivariate Time Series Forecasting with Interpretable Dynamics}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94383},
}

@inproceedings{aaai2024_multivariate_6729,
  title = {{Multivariate Time-Series Imagification with Time Embedding in Constrained Environments (Student Abstract)}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30461},
}

@inproceedings{aaai2026_t3time_106844,
  title = {{T3Time: Tri-Modal Time Series Forecasting via Adaptive Multi-Head Alignment and Residual Fusion}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39196},
}

@inproceedings{aaai2026_rethinking_107211,
  title = {{Rethinking Irregular Time Series Forecasting: A Simple Yet Effective Baseline}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39563},
}