Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data

• URL: http://arxiv.org/abs/2503.12273v1
• Date: Sat, 15 Mar 2025 22:09:39 GMT
• Title: Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
• Authors: Siddharth Rout, Eldad Haber, Stéphane Gaudreault,
• Abstract summary: This study introduces a variant of probabilistic forecasting, estimating future states as distributions rather than single-point predictions.<n>We demonstrate its effectiveness on various dynamical systems, including the challenging WeatherBench dataset.
• Score: 3.748255320979002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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