GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values

ArXi:2508.14083v3 Announce Type: replace-cross The ubiquity of missing data in urban intelligence systems, attributable to adverse environmental conditions and equipment failures, poses a significant challenge to the efficacy of downstream applications, notably in the realms of traffic forecasting and energy consumption prediction. Therefore, it is imperative to develop a robust spatio-temporal learning methodology capable of extracting meaningful insights from incomplete datasets.