SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting

no code implementations • 5 Dec 2023 • Xiaobei Zou, Luolin Xiong, Yang Tang, Jurgen Kurths

Despite the significant strides made by graph-based networks in spatio-temporal forecasting, there remain two pivotal factors closely related to forecasting performance that need further consideration: time delays in propagation dynamics and multi-scale high-dimensional interactions.

Graph Learning Graph structure learning +3