{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:26:45Z","timestamp":1775665605746,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1179","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"10621-10631","source":"Crossref","is-referenced-by-count":4,"title":["Comprehensive Review of Neural Differential Equations for Time Series Analysis"],"prefix":"10.24963","author":[{"given":"YongKyung","family":"Oh","sequence":"first","affiliation":[{"name":"University of California, Los Angeles (UCLA)"}]},{"given":"Seungsu","family":"Kam","sequence":"additional","affiliation":[{"name":"Ulsan National Institute of Science and Technology (UNIST)"}]},{"given":"Jonghun","family":"Lee","sequence":"additional","affiliation":[{"name":"Ulsan National Institute of Science and Technology (UNIST)"}]},{"given":"Dong-Young","family":"Lim","sequence":"additional","affiliation":[{"name":"Ulsan National Institute of Science and Technology (UNIST)"}]},{"given":"Sungil","family":"Kim","sequence":"additional","affiliation":[{"name":"Ulsan National Institute of Science and Technology (UNIST)"}]},{"given":"Alex A. T.","family":"Bui","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles (UCLA)"}]}],"member":"10584","event":{"name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","theme":"Artificial Intelligence","location":"Montreal, Canada","acronym":"IJCAI-2025","number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2025,8,16]]},"end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:22Z","timestamp":1758627382000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1179"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1179","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}