linear-complexity

CARNet integrates global recurrent cycle information into efficient core-based interaction modeling for multivariate time series forecasting, achieving linear complexity and outperforming strong transformer baselines on real-world benchmarks.

EMAGN is a new efficient multi-attention graph network for traffic forecasting that linearizes self-attention via learned clustering, reducing complexity from quadratic to linear while maintaining accuracy close to full-attention models, with significant reductions in training time, inference time, and GPU memory.