STOIC integrates STGNN point forecasting with tabular foundation model in-context learning for conformal prediction to quantify uncertainty in graph-structured energy time series.
Conformal inference for time series over graphs.arXiv preprint arXiv:2510.11049
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Conformal prediction generates valid sets of future conflict state sequences under a Markov assumption, providing robust uncertainty quantification compared to point predictions from likelihood methods.
citing papers explorer
-
Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models
STOIC integrates STGNN point forecasting with tabular foundation model in-context learning for conformal prediction to quantify uncertainty in graph-structured energy time series.
-
Conflict Forecasting via Conformal Prediction for Markov Processes
Conformal prediction generates valid sets of future conflict state sequences under a Markov assumption, providing robust uncertainty quantification compared to point predictions from likelihood methods.