改进流匹配中的概率路径设计，提升时序预测精度与效率
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting
- 设计新概率路径模型，优化流匹配在时序数据上的建模方式
- 在多个动力系统基准上实现更快收敛和更高预测准确率
- 推理高效，仅需少量采样步骤，适合实际应用
流匹配近年来成为生成建模的强大范式，并被拓展至潜在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尚未得到充分探索。本文表明，使用流匹配进行时空数据预测对概率路径模型的选择极为敏感。基于此洞察，我们提出一种新型概率路径模型，旨在提升预测性能。在多种动力系统基准上的实证结果表明，该模型在训练中收敛更快，预测性能优于现有路径模型。尤为重要的是，该方法在推理阶段高效，仅需少数采样步骤，使其适用于真实场景，为概率预测开辟了新路径。
原文摘要 · Abstract (English)
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.
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