Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting

• URL: http://arxiv.org/abs/2604.13253v1
• Date: Tue, 14 Apr 2026 19:33:59 GMT
• Title: Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting
• Abstract summary: Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with coverage guarantees for time series under distribution shift.<n>When a base forecaster develops persistent bias after a regime change, ACI compensates by widening intervals symmetrically, producing unnecessarily conservative bands.<n>We propose Bias-Corrected ACI (BC-ACI), which augments standard ACI with an exponentially weighted online moving average (EWM) estimate of forecast bias.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adaptive Conformal Inference (ACI) provides distribution-free prediction intervals with asymptotic coverage guarantees for time series under distribution shift. However, ACI only adapts the quantile threshold -- it cannot shift the interval center. When a base forecaster develops persistent bias after a regime change, ACI compensates by widening intervals symmetrically, producing unnecessarily conservative bands. We propose Bias-Corrected ACI (BC-ACI), which augments standard ACI with an online exponentially weighted moving average (EWM) estimate of forecast bias. BC-ACI corrects nonconformity scores before quantile computation and re-centers prediction intervals, addressing the root cause of miscalibration rather than its symptom. An adaptive dead-zone threshold suppresses corrections when estimated bias is indistinguishable from noise, ensuring no degradation on well-calibrated data. In controlled experiments across 688 runs spanning two base models, four synthetic regimes, and three real datasets, BC-ACI reduces Winkler interval scores by 13--17% under mean and compound distribution shifts (Wilcoxon p < 0.001) while maintaining equivalent performance on stationary data (ratio 1.002x). We provide finite-sample analysis showing that coverage guarantees degrade gracefully with bias estimation error.

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