arXiv (All)
open access
Role-Specific Predictive Geometries for Nonstationary Multivariate Graph-Signal Forecasting
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 role
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- TripleBound: Triplet-Guided Heterogeneous Graph Learning for Microservice Decomposition#673577
- Multimodal Signal Restoration with Signed Twofold Graph Learning#1084222
- Multi-Source Wasserstein Distributionally Robust Graph Learning#999849
- Model-to-Data Distillation for Graph Neural Networks#1144793
- BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting#1077197
Record · ID 974088
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive