Uncertainty Quantification in Non-Stationary Time Series via Adaptive Conformal Regression and Reinforcement Learning

Conformal prediction (CP) provides rigorous distribution-free uncertainty quantification, yet its effectiveness in time-series forecasting is hindered by non-stationarity and data dependencies. Recent adaptive CP methods mitigate distributional shifts by dynamically calibrating intervals based on recent residuals and adaptive weighting strategies. However, they remain limited by outlier sensitivity, undetected systematic biases, and the decoupling of calibration from model learning. In this work, we introduce CORE that establishes a mutual feedback loop between reinforcement learning (RL) and conformal prediction. By reformulating conformal regression as a sequential decision-making process, CORE utilizes RL exploration to better cover uncertain or outlier regions, adapts calibration through exploration feedback, and designs uncertainty-guided rewards, enabling dynamically improved interval quality through policy interaction. We provide theoretical analysis and conduct extensive empirical experiments to validate its effectiveness across 9 time-series standard datasets. The results demonstrate that CORE achieves superior validity and adaptivity, consistently outperforming 6 state-of-the-art baselines, with solid coverage rates and averaged 2.34% improvement in interval length.