用概率方法预测有缺失数据的动态系统，更准确可靠。
Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
- 基于随机插值法建模未来状态分布
- 在天气预测等复杂系统上表现优异
- 适合处理数据不全或含噪声的场景
动态系统建模在众多领域至关重要，但机器学习应用常受限于数据不完整或噪声干扰。本研究提出一种改进的随机插值（Stochastic Interpolation, SI）方法，用于概率性预测，将未来状态表示为概率分布而非单一数值。我们深入探讨其数学基础，并在多个动态系统上验证其有效性，包括具有挑战性的WeatherBench数据集，结果表明该方法能有效应对缺失与噪声数据，提升预测鲁棒性。
原文摘要 · Abstract (English)
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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