Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data

Siddharth Rout, Eldad Haber, Stéphane Gaudreault
3/15/2025
physics.comp-phcs.LGmath.DS

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Flow Matching for Probabilistic Learning of Dynamical Systems from Missing or Noisy Data

90% match2025

Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes

87% match2024

A Practical Probabilistic Benchmark for AI Weather Models

81% match1 repo2552024

Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks

81% match2026

Learning dynamical systems with hit-and-run random feature maps

80% match1 repo22025

Online machine-learning forecast uncertainty estimation for sequential data assimilation

80% match2023

Cite this paper

@article{rout2025probabilistic,
  title  = {Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data},
  author = {Siddharth Rout and Eldad Haber and Stéphane Gaudreault},
  year   = {2025},
  eprint = {2503.12273},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.12273v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2503.12273.svg)](https://codeofpaper.com/paper/2503.12273)

Discussion