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To handle the complexities of irregular and incomplete time series data, wepropose an invertible solution of Neural Differential Equations (NDE)-basedmethod. While NDE-based methods are a powerful method for analyzingirregularly-sampled time series, they typically do not guarantee reversibletransformations in their standard form. Our method suggests the variation ofNeural Controlled Differential Equations (Neural CDEs) with Neural Flow, whichensures invertibility while maintaining a lower computational burden.Additionally, it enables the training of a dual latent space, enhancing themodeling of dynamic temporal dynamics. Our research presents an advancedframework that excels in both classification and interpolation tasks. At thecore of our approach is an enhanced dual latent states architecture, carefullydesigned for high precision across various time series tasks. Empiricalanalysis demonstrates that our method significantly outperforms existingmodels. This work significantly advances irregular time series analysis,introducing innovative techniques and offering a versatile tool for diversepractical applications.

Invertible Solution of Neural Differential Equations for Analysis of Irregularly-Sampled Time Series