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Reliable uncertainty quantification is central to modern forecasting. Conformal prediction offers model-agnostic intervals with coverage guarantee under exchangeability, but existing software often targets general prediction problems or individual …

Conformal prediction constitutes a flexible family of methods for predictive uncertainty quantification, which has recently drawn significant attention in the statistical literature because of its distribution-free, finite-sample, and model-agnostic …