  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.
