Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data
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Proceedings of the 31st Conference On Learning Theory, PMLR 75:732749, 2018.
Abstract
We extend conformal inference to general settings that allow for time series data. Our proposal is developed as a randomization method and accounts for potential serial dependence by including block structures in the permutation scheme. As a result, the proposed method retains the exact, modelfree validity when the data are i.i.d. or more generally exchangeable, similar to usual conformal inference methods. When exchangeability fails, as is the case for common time series data, the proposed approach is approximately valid under weak assumptions on the conformity score.
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