AdaptiveConformal: An R Package for Adaptive Conformal Inference

TL;DR

AdaptiveConformal introduces an R package implementing five adaptive conformal inference algorithms that generate reliable prediction intervals for sequential data like time series, with strong theoretical guarantees and practical tools.

Contribution

The paper presents five new adaptive conformal inference algorithms, their theoretical analysis, and an open-source R package for practical application and visualization.

Findings

Algorithms perform well in simulation studies

Effective in producing prediction intervals for influenza data

Theoretical guarantees hold under sequential data conditions

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.