Kernel-based optimally weighted conformal prediction intervals

• URL: http://arxiv.org/abs/2405.16828v1
• Date: Mon, 27 May 2024 04:49:41 GMT
• Title: Kernel-based optimally weighted conformal prediction intervals
• Authors: Jonghyeok Lee, Chen Xu, Yao Xie,
• Abstract summary: We present Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI) KOWCPI adapts the classic Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data. We demonstrate the superior performance of KOWCPI on real time-series against state-of-the-art methods.
• Score: 12.814084012624916
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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