Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data
依赖数据下预测机器学习的精确与稳健共形推断
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, model-free 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…