% Key papers for Time series anomaly detection evaluation (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{aaai2018_early_99820,
  title = {{Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2018},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/11268},
}

@inproceedings{iclr2022_anomaly_31172,
  title = {{Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/7023},
}

@inproceedings{aaai2022_towards_2868,
  title = {{Towards a Rigorous Evaluation of Time-Series Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2022},
  url = {https://aaai.org/papers/07194-towards-a-rigorous-evaluation-of-time-series-anomaly-detection/},
}

@inproceedings{aaai2021_time_1511,
  title = {{Time Series Anomaly Detection with Multiresolution Ensemble Decoding}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2021},
  url = {https://aaai.org/papers/09567-time-series-anomaly-detection-with-multiresolution-ensemble-decoding/},
}

@inproceedings{neurips2024_the_72137,
  title = {{The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/97690},
}

@inproceedings{neurips2018_precision_57524,
  title = {{Precision and Recall for Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11204},
}

@inproceedings{aaai2024_when_7817,
  title = {{When Model Meets New Normals: Test-Time Adaptation for Unsupervised Time-Series Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2024},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/29210},
}

@inproceedings{iclr2022_graph_30849,
  title = {{Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2022},
  url = {https://iclr.cc/virtual/2022/poster/6770},
}

@inproceedings{iclr2024_fits_34745,
  title = {{FITS: Modeling Time Series with \$10k\$ Parameters}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18329},
}

@inproceedings{iclr2023_unsupervised_32961,
  title = {{Unsupervised Model Selection for Time Series Anomaly Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2023},
  url = {https://iclr.cc/virtual/2023/poster/11903},
}

@inproceedings{aaai2023_anoviz_3114,
  title = {{AnoViz: A Visual Inspection Tool of Anomalies in Multivariate Time Series}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2023},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/27088},
}

@inproceedings{neurips2024_ar_73714,
  title = {{AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/93784},
}

@inproceedings{icml2025_kan_46999,
  title = {{KAN-AD: Time Series Anomaly Detection with Kolmogorov–Arnold Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45584},
}

@inproceedings{neurips2021_online_62080,
  title = {{Online false discovery rate control for anomaly detection in time series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2021},
  url = {https://nips.cc/virtual/2021/poster/26656},
}

@inproceedings{neurips2025_crossad_78530,
  title = {{CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=fkYFDxiLpi},
}

@inproceedings{neurips2024_sarad_73326,
  title = {{SARAD: Spatial Association-Aware Anomaly Detection and Diagnosis for Multivariate Time Series}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94119},
}

@inproceedings{neurips2025_scatterad_77470,
  title = {{ScatterAD: Temporal-Topological Scattering Mechanism for Time Series Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=UB5u3KvOS1},
}

@inproceedings{aaai2026_harnessing_106967,
  title = {{Harnessing Vision-Language Models for Time Series Anomaly Detection}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/39319},
}

@inproceedings{icml2024_position_44563,
  title = {{Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33889},
}

@inproceedings{icml2025_when_46591,
  title = {{When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/45978},
}

@inproceedings{icml2026_anomseer_51032,
  title = {{AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=IGW4mVoDSs},
}

@inproceedings{icml2026_impact_53061,
  title = {{IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=bj4mX7nyvu},
}

@inproceedings{icml2026_cognos_55155,
  title = {{COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=w9Ml4E3Pc4},
}

@inproceedings{neurips2025_delving_75324,
  title = {{Delving into Large Language Models for Effective Time-Series Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=6rpy7X1Of8},
}

@inproceedings{neurips2025_noboom_79567,
  title = {{NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=qiLboR0ocm},
}

@inproceedings{iclr2025_towards_82621,
  title = {{Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/29176},
}

@inproceedings{iclr2026_adaptive_84893,
  title = {{Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring.}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=7uFbs68MSI},
}

@inproceedings{iclr2026_when_85673,
  title = {{When Foundation Models are One-Liners: Limitations and Future Directions for Time Series Anomaly Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=H27kvyG4qf},
}

@inproceedings{iclr2026_paano_86229,
  title = {{PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=NXThkM7Iym},
}