{"type":"ScholarlyArticle","title":"Comprehensive Review of Neural Differential Equations for Time Series Analysis","authors":[{"name":"YongKyung Oh","slug":"yongkyung-oh","given":"YongKyung","family":"Oh","affiliation":""},{"name":"Seungsu Kam","slug":"seungsu-kam","given":"Seungsu","family":"Kam","affiliation":""},{"name":"Jonghun Lee","slug":"jonghun-lee","given":"Jonghun","family":"Lee","affiliation":""},{"name":"Dong-Young Lim","slug":"dong-young-lim","given":"Dong-Young","family":"Lim","affiliation":""},{"name":"Sungil Kim","slug":"sungil-kim","given":"Sungil","family":"Kim","affiliation":""},{"name":"Alex A. T. Bui","slug":"alex-a-t-bui","given":"Alex A. T.","family":"Bui","affiliation":""}],"abstract":"Time series modeling and analysis have become critical in various domains. Conventional methods such as RNNs and Transformers, while effective for discrete-time and regularly sampled data, face significant challenges in capturing the continuous dynamics and irregular sampling patterns inherent in real-world scenarios. Neural Differential Equations (NDEs) represent a paradigm shift by combining the flexibility of neural networks with the mathematical rigor of differential equations. This paper presents a comprehensive review of NDE-based methods for time series analysis, including neural ordinary differential equations, neural controlled differential equations, and neural stochastic differential equations. We provide a detailed discussion of their mathematical formulations, numerical methods, and applications, highlighting their ability to model continuous-time dynamics. Furthermore, we address key challenges and future research directions. This survey serves as a foundation for researchers and practitioners seeking to leverage NDEs for advanced time series analysis.","doi":"10.24963/ijcai.2025/1179","url":"https://ijcai.org/proceedings/2025/1179","pdf":"https://ijcai.org/proceedings/2025/1179.pdf","markdown":"https://ijcai.org/proceedings/2025/1179.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1179","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":"Survey Track","track":"Survey Track","number":1179,"pages":{"first":10621,"last":10631},"keywords":["Machine Learning: ML: Time series and data streams","Data Mining: DM: Mining spatial and/or temporal data","Machine Learning: ML: Learning theory","Machine Learning: ML: Deep learning architectures"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}