用随机微分方程构建连续生成模型，提升交通数据预测的不确定性建模能力。
ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations
- 基于随机微分方程与扩散生成模型，在连续域中建模时空依赖性。
- 在4个交通数据集上优于现有最先进确定性和概率模型。
- 适合需要高置信度预测和不确定性量化的研究者。
准确预测时空数据仍具挑战性，源于复杂的空间依赖与时间动态。数据固有的不确定性和变异性常使确定性模型不足，促使向概率方法转变，其中基于扩散的生成模型成为有效解决方案。本文提出ProGen，一种新颖的概率时空序列预测框架，利用随机微分方程（SDEs）与连续域中的扩散生成建模技术。通过集成新型去噪得分模型、图神经网络及定制化SDE，ProGen有效捕捉时空依赖并管理不确定性。在四个基准交通数据集上的广泛实验表明，ProGen优于现有最先进确定性和概率模型。本工作为时空预测提供了连续、基于扩散的生成方法，推动概率建模与随机过程研究的发展。
原文摘要 · Abstract (English)
Accurate forecasting of spatiotemporal data remains challenging due to complex spatial dependencies and temporal dynamics. The inherent uncertainty and variability in such data often render deterministic models insufficient, prompting a shift towards probabilistic approaches, where diffusion-based generative models have emerged as effective solutions. In this paper, we present ProGen, a novel framework for probabilistic spatiotemporal time series forecasting that leverages Stochastic Differential Equations (SDEs) and diffusion-based generative modeling techniques in the continuous domain. By integrating a novel denoising score model, graph neural networks, and a tailored SDE, ProGen provides a robust solution that effectively captures spatiotemporal dependencies while managing uncertainty. Our extensive experiments on four benchmark traffic datasets demonstrate that ProGen outperforms state-of-the-art deterministic and probabilistic models. This work contributes a continuous, diffusion-based generative approach to spatiotemporal forecasting, paving the way for future research in probabilistic modeling and stochastic processes.
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