STD2Vformer: A Free-Form Spatiotemporal Forecasting Model

Spatiotemporal forecasting plays a vital role in modeling and managing complex dynamic systems, such as traffic networks, power grids, and industrial diagnostic systems. However, most existing spatiotemporal models focus primarily on improving prediction accuracy within fixed scenarios, while overlooking the challenge of adapting to dynamically changing forecasting demands. As a result, when prediction requirements shift, these models often need to be retrained to remain accurate—leading to resource inefficiency, production delays, and heightened safety risks. To address this issue, we propose a novel spatiotemporal prediction framework that effectively captures dynamic spatiotemporal dependencies and can be directly applied to spatiotemporal prediction tasks with varying prediction lengths and arbitrary starting points, requiring only a single training phase. Specifically, we introduce the spatiotemporal Date2Vec embedding method, which generates past and future timestamp embeddings by explicitly modeling intrinsic spatiotemporal relationships. Furthermore, we design a fusion module to model the direct mapping relationship between past and future timestamp embeddings, thereby enabling rapid adaptation to dynamic prediction demands. Extensive experiments on seven real-world public datasets show that our model exhibits superior adaptability across four distinct domains and higher predictive accuracy—achieving an average 4.55% improvement over the best-performing baseline on the fixed-horizon prediction task and an average 9.10% improvement on the free-form prediction task—while also providing lower computational complexity and faster inference compared with state-of-the-art methods.