Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
This addresses forecasting challenges in fields like meteorology, but it appears incremental as a variant of an existing method.
The paper tackled probabilistic forecasting for dynamical systems with missing or imperfect data by introducing a variant of stochastic interpolation, resulting in effective distribution-based predictions demonstrated on datasets like WeatherBench.
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.