Kernel-based Optimally Weighted Conformal Time-Series Prediction
Jonghyeok Lee Chen Xu Yao Xie
Conformal prediction has been a popular distribution-free framework for uncertainty quantification. In this work, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals ($\texttt{KOWCPI}$). Specifically, $\texttt{KOWCPI}$ adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data and learns optimal data-adaptive weights. Theoretically, we tackle the challenge of establishing a conditional coverage guarantee for non-exchangeable data under strong mixing conditions on the non-conformity scores. We demonstrate the superior performance of $\texttt{KOWCPI}$ on real time-series against state-of-the-art methods, where $\texttt{KOWCPI}$ achieves narrower confidence intervals without losing coverage.
Conformal prediction, Nonparametric kernel regression, Time series
A sequential conformal prediction method for time-series through nonparametric kernel regression, with rigorous theoretical analysis and strong empirical performances.