Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data

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{rout2025_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 }
}