Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data

Siddharth Rout; Eldad Haber; Stéphane Gaudreault

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arXiv · 2503.12273

Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data

Siddharth Rout·Eldad Haber·Stéphane Gaudreault

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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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physics.comp-ph cs.LG math.DS physics.ao-ph

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Siddharth Rout, Eldad Haber, Stéphane Gaudreault. 2025-03-15. Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data. https://arxiv.org/abs/2503.12273

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