---
title: "Comprehensive Review of Neural Differential Equations for Time Series Analysis"
authors:
  - "YongKyung Oh"
  - "Seungsu Kam"
  - "Jonghun Lee"
  - "Dong-Young Lim"
  - "Sungil Kim"
  - "Alex A. T. Bui"
doi: "10.24963/ijcai.2025/1179"
url: "https://ijcai.org/proceedings/2025/1179"
pdf: "https://ijcai.org/proceedings/2025/1179.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10621-10631"
track: "Survey Track"
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"
---

# Comprehensive Review of Neural Differential Equations for Time Series Analysis

YongKyung Oh, Seungsu Kam, Jonghun Lee, Dong-Young Lim, Sungil Kim, Alex A. T. Bui

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10621-10631. DOI: [10.24963/ijcai.2025/1179](https://doi.org/10.24963/ijcai.2025/1179)

## 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.