No AI summary available for this article.
Why It Matters
Forecasting multivariate graph signals is challenging when node-level trajectories are nonstationary but stable relations persist across nodes and features.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
Forecasting multivariate graph signals is challenging when node-level trajectories are nonstationary but stable relations persist across nodes and features. In an error-correction representation, long-run equilibrium restoration and short-run transient propagation represent different predictive roles and need not share a common cross-feature geometry. We introduce role-specific predictive geometries in which directed Long relations act on estimated equilibrium coordinates, whereas directed Short relations act on lagged differences. Matrix-valued Long responses mix equilibrium coordinates befor...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.06519v1 · Indexed 4 days ago