Nested Spatio-Temporal Time Series Forecasting

ArXi:2605.16447v1 Announce Type: new Spatiotemporal forecasting is critical for real-world applications like traffic management, yet capturing reliable interactions remains challenging under noisy and non-stationary conditions. Existing methods primarily rely on historical spatial priors, often failing to account for evolving temporal correlations and suffering from systematic errors.