To handle the complexities of irregular and incomplete time series data, we propose an invertible solution of Neural Differential Equations (NDE)-based method. While NDE-based methods are a powerful method for analyzing irregularly-sampled time series, they typically do not guarantee reversible transformations in their standard form. Our method suggests the variation of Neural Controlled Differential Equations (Neural CDEs) with Neural Flow, which ensures invertibility while maintaining a lower computational burden. Additionally, it enables the training of a dual latent space, enhancing the modeling of dynamic temporal dynamics. Our research presents an advanced framework that excels in both classification and interpolation tasks. At the core of our approach is an enhanced dual latent states architecture, carefully designed for high precision across various time series tasks. Empirical analysis demonstrates that our method significantly outperforms existing models. This work significantly advances irregular time series analysis, introducing innovative techniques and offering a versatile tool for diverse practical applications.

翻译：为处理不规则和不完整时间序列数据的复杂性，我们提出了一种基于神经微分方程(NDE)的可逆解方法。尽管基于NDE的方法是不规则采样时间序列分析的有力工具，但其标准形式通常无法保证变换的可逆性。我们的方法提出将神经受控微分方程(Neural CDEs)与神经流(Neural Flow)相结合，在确保可逆性的同时保持较低的计算负担。此外，该方法能够训练双重潜在空间，从而增强对动态时序动态特性的建模能力。我们提出的先进框架在分类与插值任务中均表现卓越。该方法的核心是精心设计的高精度增强型双重潜在状态架构，可适用于多种时间序列任务。实证分析表明，我们的方法显著优于现有模型。本研究推动了不规则时间序列分析领域的重大进展，引入了创新技术，并为多样化实际应用提供了通用工具。