{"type":"ScholarlyArticle","title":"Towards Comprehensive Benchmarking of Domain Generalization and Adaptation for Time-Series Forecasting in Clinical Multivariate Time-Series Forecasting","authors":[{"name":"Mayra Elwes","slug":"mayra-elwes","given":"Mayra","family":"Elwes","affiliation":""}],"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.","doi":"10.24963/ijcai.2026/942","url":"https://ijcai.org/proceedings/2026/942","pdf":"https://ijcai.org/proceedings/2026/0942.pdf","markdown":"https://ijcai.org/proceedings/2026/942.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/942","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":942,"pages":{"first":8327,"last":8328},"keywords":["Multidisciplinary Topics and Applications: Health and medicine","Machine Learning: Evaluation","AI Ethics, Trust, Fairnes: Safety and robustness","AI: Machine Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}