ProGen：从连续生成视角利用随机微分方程重新审视概率时空时间序列预测
ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations
AI总结：
ProGen提出利用随机微分方程和扩散生成模型进行概率时空预测，通过整合去噪分数模型与图神经网络，在四个交通数据集上超越现有确定性与概率模型。
摘要：
由于复杂的空间依赖性和时间动态性，时空数据的准确预测仍然具有挑战性。此类数据固有的不确定性和变异性往往使确定性模型不足，促使研究转向概率方法，其中基于扩散的生成模型已成为有效的解决方案。在本文中，我们提出了ProGen，一个用于概率时空时间序列预测的新颖框架，该框架利用连续域中的随机微分方程（SDEs）和基于扩散的生成建模技术。通过整合新颖的去噪分数模型、图神经网络和定制的SDE，ProGen提供了一种稳健的解决方案，能有效捕获时空依赖性同时管理不确定性。我们在四个基准交通数据集上的广泛实验表明，ProGen优于最先进的确定性和概率模型。这项工作为时空预测贡献了一种连续的、基于扩散的生成方法，为未来在概率建模和随机过程方面的研究铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
