Towards Comprehensive Benchmarking of Domain Generalization and Adaptation for Time-Series Forecasting in Clinical Multivariate Time-Series Forecasting

Elwes, Mayra

doi:10.24963/ijcai.2026/942

Towards Comprehensive Benchmarking of Domain Generalization and Adaptation for Time-Series Forecasting in Clinical Multivariate Time-Series Forecasting

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Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 8327-8328.

https://doi.org/10.24963/ijcai.2026/942

Keywords

Multidisciplinary Topics and Applications: Health and medicine
Machine Learning: Evaluation
AI Ethics, Trust, Fairnes: Safety and robustness
AI: Machine Learning

Abstract

Deep learning models for clinical time-series forecasting face significant challenges in generalizability when deployed across different clinical settings. Various choices in the deep learning pipeline can affect a model's out-of-distribution performance. This abstract introduces a comprehensive benchmarking framework to assess domain generalization and adaptation under varying distribution shifts in multivariate time-series forecasting using real-world data. Preliminary empirical results on the influence of backbone model architecture and a proxy metric, the robustness slope, are presented alongside remaining challenges.

Mayra Elwes. 2026. Towards Comprehensive Benchmarking of Domain Generalization and Adaptation for Time-Series Forecasting in Clinical Multivariate Time-Series Forecasting. In Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26. IJCAI, 8327–8328. https://doi.org/10.24963/ijcai.2026/942

@inproceedings{ijcai2026p942,
  title     = {Towards Comprehensive Benchmarking of Domain Generalization and Adaptation for Time-Series Forecasting in Clinical Multivariate Time-Series Forecasting},
  author    = {Elwes, Mayra},
  booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
               Artificial Intelligence, {IJCAI-26}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Diego Calvanese},
  pages     = {8327--8328},
  year      = {2026},
  month     = {8},
  note      = {Doctoral Consortium},
  doi       = {10.24963/ijcai.2026/942},
  url       = {https://doi.org/10.24963/ijcai.2026/942},
}

TY  - CPAPER
TI  - Towards Comprehensive Benchmarking of Domain Generalization and Adaptation for Time-Series Forecasting in Clinical Multivariate Time-Series Forecasting
AU  - Elwes, Mayra
T2  - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2026
SP  - 8327
EP  - 8328
DO  - 10.24963/ijcai.2026/942
UR  - https://doi.org/10.24963/ijcai.2026/942
ER  -