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.

基于神经微分方程的可逆解决方案对处理不规则和不完整的时间序列数据进行复杂性处理，使用神经控制微分方程和神经流的变体确保可逆变换，并通过增强的双潜在状态体系提供优越的分类和插值任务的模型建模，实证分析表明该方法明显优于现有模型，推进了不规则时间序列分析，并为多样的实际应用提供了一种多功能工具。