Abstract
In this work we provide a review of basic ideas and novel developments about Conformal Prediction — an innovative distribution-free, non-parametric forecasting method, based on minimal assumptions — that is able to yield in a very straightforward way prediction sets that are valid in a statistical sense also in the finite sample case. The discussion provided in the paper covers the theoretical underpinnings of Conformal Prediction, and then proceeds to list the more advanced developments and adaptations of the original idea.
Funding Statement
The authors acknowledge financial support from: Accordo Quadro ASI-POLIMI “Attività di Ricerca e Innovazione” n. 2018-5-HH.0, collaboration agreement between the Italian Space Agency and Politecnico di Milano; the European Research Council, ERC grant agreement no 336155-project COBHAM “The role of consumer behaviour and heterogeneity in the integrated assessment of energy and climate policies”; the “Safari Njema Project - From informal mobility to mobility policies through big data analysis”, funded by Polisocial Award 2018 - Politecnico di Milano.
Acknowledgements
The authors would also like to thank two anonymous reviewers for the invaluable suggestions provided.
Citation
Matteo Fontana. Gianluca Zeni. Simone Vantini. "Conformal prediction: A unified review of theory and new challenges." Bernoulli 29 (1) 1 - 23, February 2023. https://doi.org/10.3150/21-BEJ1447
