Kernel-based optimally weighted conformal prediction intervals
<|reference_start|>Kernel-based optimally weighted conformal prediction intervals: Conformal prediction has been a popular distribution-free framework for uncertainty quantification. In this paper, we present a novel conformal prediction method for time-series, which we call Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI). Specifically, 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 KOWCPI on real time-series against state-of-the-art methods, where KOWCPI achieves narrower confidence intervals without losing coverage.<|reference_end|>
@article{lee2024kernel-based,
    title={Kernel-based optimally weighted conformal prediction intervals},
    author={Jonghyeok Lee, Chen Xu, Yao Xie},
    journal={arXiv preprint arXiv:2405.16828},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16828},
    primaryClass={cs.LG math.ST stat.ML stat.TH}
}