In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphs

The paper introduces STOIC, a post‑hoc residual calibration framework that enhances uncertainty quantification for energy time series over graphs. STOIC combines spatial‑temporal graph neural network forecasts with in‑context learning from tabular foundation models to estimate feature‑conditional residual quantiles, thereby capturing both sequential and relational dependencies. It is designed as a conformal‑inspired method that does not require task‑specific retraining of the calibration model.

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