2022

Adaptive Conformal Predictions for Time Series

Zaffran, Margaux, Dieuleveut, Aymeric, Féron, Olivier et al.

Understand

Uncertainty quantification of predictive models is crucial in decision-making problems.

Conformal prediction is a general and theoretically sound answer.
However, it requires exchangeable data, excluding time series.
While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency.

Built on

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