Abstract Uncertainty quantification in functional data classification is crucial for reliable decision‐making but remains insufficiently addressed in existing methods, which predominantly focus on point predictions. In this paper we propose a novel method for constructing prediction sets within the conformal prediction framework. Our approach begins by improving the construction of functional prediction bands, enhancing their ability to capture the shape characteristics of functional data. Based on these refined bands, we introduce a new nonconformity measure that quantifies the signed distance between a curve and the class‐specific prediction bands. This enables us to construct label prediction sets with valid coverage guarantees at a given confidence level. Extensive simulation studies and real data experiments demonstrate that the proposed method yields prediction sets with high coverage and small ambiguity, effectively quantifying uncertainty while maintaining strong classification performance.
Hao et al. (Mon,) studied this question.