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This paper addresses a critical gap in applying univariate foundation models to multi-station weather forecasting by explicitly modeling spatial station information and distinct error priors. The proposed WxFM-XL architecture utilizes a dynamic fusion mechanism to integrate spatial correlations with error correlation graphs, demonstrating superior performance over existing baselines on multiple datasets. This work advances the practical deployment of time series foundation models in complex, geographically distributed environments.
WxFM-XL: Adapting Univariate Foundation Models to Multi-Station Weather Forecasting
arXiv:2610.10057v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG)
arXiv:2610.10057 [cs.LG]
(or arXiv:2610.10057v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.10057 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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