{"type":"ScholarlyArticle","title":"Toward Interpretable Time Series Modeling: A Kernel Representation Perspective","authors":[{"name":"Kunpeng Xu","slug":"kunpeng-xu","given":"Kunpeng","family":"Xu","affiliation":""}],"abstract":"Time series modeling is essential in finance, healthcare, and environmental science, yet nonlinear patterns, noise, and concept drift pose challenges. Although deep learning models, such as Transformer-based and recent pre-trained models, have achieved good performance across various time series tasks, they often lack interpretability, especially in co-evolving time series. This work introduces a kernel representation learning (KRL) perspective, rethinking time series modeling through kernel-induced self-representation to effectively capture temporal structures and dynamic transitions. Additionally, we establish theoretical connections between KRL and advanced deep-network models, demonstrating how kernel methods provide a principled approach to capturing complex time series behaviors.","doi":"10.24963/ijcai.2025/1245","url":"https://ijcai.org/proceedings/2025/1245","pdf":"https://ijcai.org/proceedings/2025/1245.pdf","markdown":"https://ijcai.org/proceedings/2025/1245.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1245","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":1245,"pages":{"first":10981,"last":10982},"keywords":["Data Mining: DM: Mining spatial and/or temporal data","Machine Learning: ML: Time series and data streams","Data Mining: DM: Mining data streams"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}