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

@inproceedings{aaai2016_online_105085,
  title = {{Online ARIMA Algorithms for Time Series Prediction}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10257},
}

@inproceedings{aaai2023_dish_3480,
  title = {{Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/25914},
}

@inproceedings{neurips2016_bayesian_55594,
  title = {{Bayesian Intermittent Demand Forecasting for Large Inventories}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2016},
  url = {https://nips.cc/virtual/2016/poster/7225},
}

@inproceedings{aaai2021_temporal_1476,
  title = {{Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09117-temporal-latent-auto-encoder-a-method-for-probabilistic-multivariate-time-series-forecasting/},
}

@inproceedings{iclr2021_multivariate_30385,
  title = {{Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2021},
  url = {https://iclr.cc/virtual/2021/poster/3117},
}

@inproceedings{neurips2020_normalizing_60439,
  title = {{Normalizing Kalman Filters for Multivariate Time Series Analysis}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18812},
}

@inproceedings{aaai2021_dynamic_476,
  title = {{Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/00651-dynamic-gaussian-mixture-based-deep-generative-model-for-robust-forecasting-on-sparse-multivariate-time-series/},
}

@inproceedings{aaai2016_predicting_105442,
  title = {{Predicting Spatio-Temporal Propagation of Seasonal Influenza Using Variational Gaussian Process Regression}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2016},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/9899},
}

@inproceedings{neurips2017_non_56515,
  title = {{Non-Stationary Spectral Kernels}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9241},
}

@inproceedings{neurips2023_koopa_67146,
  title = {{Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/72562},
}

@inproceedings{neurips2019_high_58373,
  title = {{High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/13787},
}

@inproceedings{icml2016_automatic_35747,
  title = {{Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2016},
  url = {https://proceedings.mlr.press/v48/hwangb16.html},
}

@inproceedings{icml2021_end_39297,
  title = {{End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2021},
  url = {https://icml.cc/virtual/2021/poster/10001},
}

@inproceedings{neurips2023_conformal_70078,
  title = {{Conformal PID Control for Time Series Prediction}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/69896},
}

@inproceedings{neurips2023_onenet_68055,
  title = {{OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2023},
  url = {https://nips.cc/virtual/2023/poster/71725},
}

@inproceedings{aaai2024_conformal_5469,
  title = {{Conformal Prediction Regions for Time Series Using Linear Complementarity Programming}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/30089},
}

@inproceedings{icml2017_nonnegative_36310,
  title = {{Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/522},
}

@inproceedings{aaai2021_synergetic_1454,
  title = {{Synergetic Learning of Heterogeneous Temporal Sequences for Multi-Horizon Probabilistic Forecasting}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/08420-synergetic-learning-of-heterogeneous-temporal-sequences-for-multi-horizon-probabilistic-forecasting/},
}

@inproceedings{icml2022_adaptive_40220,
  title = {{Adaptive Conformal Predictions for Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2022},
  url = {https://icml.cc/virtual/2022/poster/17817},
}

@inproceedings{neurips2020_deep_59658,
  title = {{Deep Rao-Blackwellised Particle Filters for Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/18625},
}

@inproceedings{aaai2024_cumulative_5535,
  title = {{Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29266},
}

@inproceedings{iclr2024_copula_35266,
  title = {{Copula Conformal prediction for multi-step time series prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/17807},
}

@inproceedings{icml2024_conformal_45622,
  title = {{Conformal prediction for multi-dimensional time series by ellipsoidal sets}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32830},
}

@inproceedings{icml2024_an_45755,
  title = {{An Analysis of Linear Time Series Forecasting Models}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/32697},
}

@inproceedings{icml2025_risk_46741,
  title = {{Risk and cross validation in ridge regression with correlated samples}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45831},
}

@inproceedings{icml2025_retrieval_46746,
  title = {{Retrieval Augmented Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45826},
}

@inproceedings{neurips2024_analysing_71255,
  title = {{Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95988},
}

@inproceedings{neurips2024_conformalized_71621,
  title = {{Conformalized Time Series with Semantic Features}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95653},
}

@inproceedings{neurips2024_multivariate_72976,
  title = {{Multivariate Probabilistic Time Series Forecasting with Correlated Errors}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94440},
}

@inproceedings{neurips2025_conformal_76276,
  title = {{Conformal Prediction for Time-series Forecasting with Change Points}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=HgLaVgCpCl},
}

@inproceedings{aaai2025_mixture_9967,
  title = {{Mixture of Online and Offline Experts for Non-Stationary Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34448},
}

@inproceedings{icml2025_relational_49023,
  title = {{Relational Conformal Prediction for Correlated Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43601},
}

@inproceedings{neurips2024_chronoepilogi_74542,
  title = {{ChronoEpilogi: Scalable Time Series Selection with Multiple Solutions}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93042},
}

@inproceedings{iclr2025_kernel_83455,
  title = {{Kernel-based Optimally Weighted Conformal Time-Series Prediction}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/28361},
}

@inproceedings{aaai2025_kernelmatmul_9640,
  title = {{KernelMatmul: Scaling Gaussian Processes to Large Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33893},
}

@inproceedings{icml2024_time_44441,
  title = {{Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34011},
}

@inproceedings{icml2024_conformalized_45337,
  title = {{Conformalized Adaptive Forecasting of Heterogeneous Trajectories}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33115},
}

@inproceedings{icml2025_winner_46073,
  title = {{Winner-takes-all for Multivariate Probabilistic Time Series Forecasting}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/46485},
}

@inproceedings{icml2025_learning_49076,
  title = {{Learning with Expected Signatures: Theory and Applications}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/43548},
}