Gong, M., Chen, L., & Li, J. (2024). ProGen: Revisiting Probabilistic Spatial-Temporal Time Series Forecasting from a Continuous Generative Perspective Using Stochastic Differential Equations. arXiv preprint arXiv:2411.01267.
This paper introduces ProGen, a novel framework for probabilistic spatiotemporal time series forecasting, aiming to address the limitations of existing deterministic models in capturing uncertainty and the computational challenges of autoregressive diffusion models.
ProGen employs a two-step process: a forward diffusion process that gradually perturbs future ground truth data into a Gaussian distribution while training a score model, and a reverse prediction process that iteratively denoises samples from this Gaussian distribution to generate a forecasting distribution. The framework leverages a tailored spatiotemporal SDE, incorporating spatial dependencies through an adjacency matrix, and an adaptive mechanism to optimize denoising across diffusion steps.
ProGen offers a robust and efficient approach to probabilistic spatiotemporal time series forecasting, effectively capturing uncertainty and spatial dependencies in the data. The framework's continuous-time generative modeling perspective and tailored SDE contribute significantly to its superior performance.
This research advances the field of spatiotemporal forecasting by introducing a novel framework that combines the strengths of deterministic and probabilistic approaches. ProGen's ability to provide accurate predictions with quantified uncertainty has significant implications for various applications, including traffic flow prediction, weather forecasting, and epidemic modeling.
Future work could focus on further improving ProGen's inference efficiency and exploring its applications in other domains beyond traffic forecasting. Additionally, investigating the impact of different graph structures and incorporating external factors into the model could enhance its applicability and performance.
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by Mingze Gong,... at arxiv.org 11-05-2024
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