Error-quantified Conformal Inference for Time Series

Junxi Wu, Dongjian Hu, Yajie Bao, Shu-Tao Xia, Changliang Zou

Keywords: Time Series; Uncertainty Quantification; Conformal Prediction; Distribution Shift

TL;DR: We propose a new online conformal inference method ECI by quantifying the extent of under/over coverage, which can react quickly to distribution shift in time series.

Abstract: Uncertainty quantification in time series prediction is challenging due to the temporal dependence and distribution shift on sequential data. Conformal prediction provides a pivotal and flexible instrument for assessing the uncertainty of machine learning models through prediction sets. Recently, a series of online conformal inference methods updated thresholds of prediction sets by performing online gradient descent on a sequence of quantile loss functions. A drawback of such methods is that they only use the information of revealed non-conformity scores via miscoverage indicators but ignore error quantification, namely the distance between the non-conformity score and the current threshold. To accurately leverage the dynamic of miscoverage error, we propose Error-quantified Conformal Inference (ECI) by smoothing the quantile loss function. ECI introduces a continuous and adaptive feedback scale with the miscoverage error, rather than simple binary feedback in existing methods. We establish a long-term coverage guarantee for ECI under arbitrary dependence and distribution shift. The extensive experimental results show that ECI can achieve valid miscoverage control and output tighter prediction sets than other baselines.

Primary Area: learning on time series and dynamical systems

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Submission Number: 3886