AI 中文总结
本文提出WxFM-XL，通过跨站点误差相关先验图和动态融合机制，将单变量时间序列基础模型适配到多站点天气预报，并在多个数据集上超越现有基线。
AI 中文摘要
随着单变量时间序列基础模型（如Sundial、Timer）的兴起，已有初步工作尝试将其扩展到多变量场景。然而，这些模型主要侧重于变量间相关性的建模。当它们被应用于多站点天气预报时，两个重要因素常被忽视：（1）站点的空间信息，（2）不同站点相对于基础模型的不同误差先验。在本文中，我们提出WxFM-XL，一个将单变量时间序列基础模型适配到多站点天气预报的模型。WxFM-XL引入了一个跨站点误差相关先验图，以捕获相对于基础模型的站点级误差先验。在此基础上，我们进一步提出了一种动态融合机制，该机制自适应地将空间相关图与误差相关先验图进行整合。在多个数据集上的实验表明，我们的模型优于最先进的基线方法。
英文摘要
With the rise of univariate time series foundation models (e.g., Sundial, Timer), initial efforts have been made to extend them to multivariate settings. However, these models mainly focus on modeling correlations among variables. When they are applied to multi-station weather forecasting, two important factors are often overlooked: (1) the spatial information of stations, and (2) different error priors of different stations relative to the foundation model. In this paper, we propose WxFM-XL, a model for adapting univariate time series foundation models to multi-station weather forecasting. WxFM-XL introduces a cross-station error correlation prior graph to capture stationwise error priors with respect to the foundation model. Building on this, we further propose a dynamic fusion mechanism that adaptively integrates a spatial correlation graph with the error correlation prior graph. Experiments on multiple datasets demonstrate that our model outperforms state of the art baselines.
