Distribution-Free Predictive Inference under Unknown Temporal Drift

Elise Han; Chengpiao Huang; Kaizheng Wang

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arXiv · 2406.06516

Distribution-Free Predictive Inference under Unknown Temporal Drift

Elise Han·Chengpiao Huang·Kaizheng Wang

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Abstract

Distribution-free prediction sets play a pivotal role in uncertainty quantification for complex statistical models. Their validity hinges on reliable calibration data, which may not be readily available as real-world environments often undergo unknown changes over time. In this paper, we propose a strategy for choosing an adaptive window and use the data therein to construct prediction sets. The window is selected by optimizing an estimated bias-variance tradeoff. We provide sharp coverage guarantees for our method, showing its adaptivity to the underlying temporal drift. We also illustrate its efficacy through numerical experiments on synthetic and real data.

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Elise Han, Chengpiao Huang, Kaizheng Wang. 2024-06-10. Distribution-Free Predictive Inference under Unknown Temporal Drift. https://arxiv.org/abs/2406.06516

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