  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.
