Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data

Fuente: arXiv
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Main Authors: Rout, Siddharth, Haber, Eldad, Gaudreault, Stéphane
Format: Preprint
Published: 2025
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author Rout, Siddharth
Haber, Eldad
Gaudreault, Stéphane
author_facet Rout, Siddharth
Haber, Eldad
Gaudreault, Stéphane
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_12273
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
Rout, Siddharth
Haber, Eldad
Gaudreault, Stéphane
Computational Physics
Machine Learning
Dynamical Systems
Atmospheric and Oceanic Physics
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.
title Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
topic Computational Physics
Machine Learning
Dynamical Systems
Atmospheric and Oceanic Physics
url https://arxiv.org/abs/2503.12273