Neural Differential Equations for Continuous-Time Analysis

Abstract

Modeling complex, irregular time series is a critical challenge in knowledge discovery and data mining. This tutorial introduces Neural Differential Equations (NDEs) - a powerful paradigm for continuous-time deep learning that intrinsically handles the non-uniform sampling and missing values where traditional models falter. We provide a comprehensive review of the theory and practical application of the entire NDE family: Neural Ordinary (NODEs), Controlled (NCDEs), and Stochastic (NSDEs) Differential Equations. The tutorial emphasizes robustness and stability and culminates in a hands-on session where participants will use key open-source libraries to solve real-world tasks like interpolation and classification. Designed for AI researchers and practitioners, this tutorial equips attendees with essential tools for time series analysis.