arXiv (All)
open access
Structure-Aware Graph Abstention for Reliable Selective Forecasting
Selective forecasting abstains on high-risk test windows under a retained-coverage budget. Existing gates such as TEM (Brusokas et al., 2025) score each forecast as a whole; for multivariate outputs, trajectories can look plausible while violating dependencies among variables. We treat instance-leve
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
- Global tree forecasters collapse at the hierarchical aggregate: a five-panel failure characterization#1064456
- Preemptive Ensemble Forecast Sensitivity to Observations#1000254
- RAID: Semantic Graph Diffusion for True Cold-Start and Cross-Lingual Forecasting#280204
- HART: Hybrid Attention-RWKV Time series Foundation Model#1296890
- Every Fixed Metric Has a Blind Spot: A Learned Atmospheric Critic for Scoring Forecast Realism#1012567
Record · ID 1218057
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