Conformal Prediction and Distribution-Free Uncertainty Quantification

Matteo Fontana

Foresight: The International Journal of Applied Forecasting, 2026, issue 82, 46-52

Abstract: While forecast uncertainty has traditionally been quantified through the lens of classical statistics, these methods assume a known probability distribution - usually the normal curve. Since real-world data often violate these distribution assumptions, conformal prediction provides an alternative offering: a distribution-free approach to uncertainty quantification. Copyright International Institute of Forecasters, 2026

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:for:ijafaa:y:2026:i:82:p:46-52

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