ResoPath: Disentangling Multi-Scale Continuous Paths with Missingness-Aware Neural CDEs for Irregular Time Series Forecasting

Irregularly sampled multivariate time series play a pivotal role across science and engineering, yet non-uniform observation intervals and severe missingness make continuous dynamic modeling challenging. Existing neural controlled differential equation based methods collapse sparse observations into single interpolated paths, conflating multi-scale patterns with synthetic smoothing artifacts and ignoring observation reliability. In this paper, we introduce ResoPath, an end-to-end framework that constructs multi-resolution continuous control paths by disentangling coarse temporal trends from fine-scale fluctuations. ResoPath couples wavelet decomposition with frequency-conditioned denoising to construct continuous control paths that drive multiscale latent dynamics through vector fields conditioned on missingness-aware information. Extensive comparisons with diverse baselines across five real-world datasets demonstrate that ResoPath achieves state-of-the-art accuracy and strong robustness in the irregular multivariate time series forecasting task.