Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression

ArXi:2603.10410v1 Announce Type: cross Spatio-temporal time series are widely used in real-world applications, including traffic prediction and weather forecasting. They are sequences of observations over extensive periods and multiple locations, naturally represented as multidimensional data. Forecasting is a central task in spatio-temporal analysis, and numerous deep learning methods have been developed to address it. However, as dataset sizes and model complexities continue to grow in practice