MDCP constructs conformal prediction intervals for Markov processes with non-asymptotic unconditional coverage bounds under beta-mixing and asymptotic conditional validity using kernel estimators and PIT to iid-ify the data.
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Distributional Conformal Prediction for Markov Processes
MDCP constructs conformal prediction intervals for Markov processes with non-asymptotic unconditional coverage bounds under beta-mixing and asymptotic conditional validity using kernel estimators and PIT to iid-ify the data.