SA-BCP adaptively blends temporal Bayesian predictions with spatial KDE evidence via threshold K, derives closed-form MSE-optimal K, and provides an online selection procedure with regret bounds, yielding sharper intervals at nominal coverage on volatility and weather data.
arXiv preprint arXiv:2412.01098 , year=
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STOIC integrates STGNN point forecasting with tabular foundation model in-context learning for conformal prediction to quantify uncertainty in graph-structured energy time series.
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Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction
SA-BCP adaptively blends temporal Bayesian predictions with spatial KDE evidence via threshold K, derives closed-form MSE-optimal K, and provides an online selection procedure with regret bounds, yielding sharper intervals at nominal coverage on volatility and weather data.
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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.