AdaptiveConformal: An R Package for Adaptive Conformal Inference
Abstract: Conformal Inference (CI) is a popular approach for generating finite sample prediction intervals based on the output of any point prediction method when data are exchangeable. Adaptive Conformal Inference (ACI) algorithms extend CI to the case of sequentially observed data, such as time series, and exhibit strong theoretical guarantees without having to assume exchangeability of the observed data. The common thread that unites algorithms in the ACI family is that they adaptively adjust the width of the generated prediction intervals in response to the observed data. We provide a detailed description of five ACI algorithms and their theoretical guarantees, and test their performance in simulation studies. We then present a case study of producing prediction intervals for influenza incidence in the United States based on black-box point forecasts. Implementations of all the algorithms are released as an open-source R package, AdaptiveConformal, which also includes tools for visualizing and summarizing conformal prediction intervals.
Repository Url: https://github.com/herbps10/aci-paper
Changes Since Last Submission: We would like to thank both of the reviewers for their careful reading of the manuscript and for their insightful comments. We respond to all comments. Unfortunately RMarkdown doesn't make it easy to indicate via text color where we made revisions in the manuscript, but we note that because the manuscript source is on Github (github.com/herbps10/aci-paper) it is possible to use the Github diff tools to view changes between the source codes of the original and revised manuscripts.
Assigned Action Editor: ~Mathurin_Massias1
Submission Number: 5
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