arXiv (All)
open access
Interpretable Graph Partitioning for Spatial Prediction
Spatial prediction estimates unobserved values from spatially distributed observations. Although flexible predictive models are increasingly available, their practical use remains limited when interpretability is essential. In this study, we formulate a graph partitioning problem to construct a glob
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
- Surrogate Graph Partitioning for Spatial Prediction#1296984
- Graph-Based Machine Learning for Multivariate Time Series Prediction in Scientific Domains: Streamflow Forecasting and Solar Flare Prediction#429891
- Modular Deep Learning Mechanisms for Auditable Next-Day Wildfire Spread Prediction#1035614
- Just for FUNS: LLM-Guided Spatio-Temporal Graph Node Generation for Forecasting Unobserved Node States#1232857
- VaryST: Generalizable Spatio-Temporal Forecasting Beyond the Closed-World Setting#1354497
Record · ID 1327653
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