Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic time series forecasting in latent spaces. However, the impact of the specific choice of probability path model on forecasting performance remains under-explored. In this work, we demonstrate that forecasting spatio-temporal data with flow matching is highly sensitive to the selection of the probability path model. Motivated by this insight, we propose a novel probability path model designed to improve forecasting performance. Our empirical results across various dynamical system benchmarks show that our model achieves faster convergence during training and improved predictive performance compared to existing probability path models. Importantly, our approach is efficient during inference, requiring only a few sampling steps. This makes our proposed model practical for real-world applications and opens new avenues for probabilistic forecasting.

翻译：流匹配最近已成为生成建模的强大范式，并已扩展到潜空间中的概率时间序列预测。然而，概率路径模型的具体选择对预测性能的影响仍未得到充分探索。在这项工作中，我们证明了使用流匹配预测时空数据对概率路径模型的选择高度敏感。基于这一见解，我们提出了一种旨在提高预测性能的新型概率路径模型。我们在各种动态系统基准上的实证结果表明，与现有的概率路径模型相比，我们的模型在训练期间实现了更快的收敛速度和更高的预测性能。重要的是，我们的方法在推理过程中是高效的，仅需少量采样步骤。这使得我们提出的模型对于实际应用具有实用性，并为概率预测开辟了新的途径。