The modeling of dynamical systems is essential in many fields, but applying machine learning techniques is often challenging due to incomplete or noisy data. This study introduces a variant of stochastic interpolation (SI) for probabilistic forecasting, estimating future states as distributions rather than single-point predictions. We explore its mathematical foundations and demonstrate its effectiveness on various dynamical systems, including the challenging WeatherBench dataset.
@article{arxiv.2503.12273,
title = {Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data},
author = {Siddharth Rout and Eldad Haber and Stéphane Gaudreault},
journal= {arXiv preprint arXiv:2503.12273},
year = {2025}
}