Rolling-Origin Conformal Prediction under Local Stationarity and Weak Dependence
Stanis{\l}aw M. S. Halkiewicz

TL;DR
This paper develops and analyzes a rolling-origin conformal prediction method for time-series forecasting, adapting to dependence and distributional changes, with theoretical optimality and empirical validation.
Contribution
It introduces a theoretically grounded, adaptive calibration window for conformal prediction in time series, achieving minimax-optimal coverage error rates under local stationarity.
Findings
Rolling-origin calibration outperforms full-history calibration in 86% of tests.
Maintains coverage within ±2% of the target at various horizons.
Empirical slope of 0.614 supports the theoretical 2/3 rate.
Abstract
We propose and analyse rolling-origin conformal prediction for time-series forecasting. The method calibrates the conformal quantile against the most recent pseudo-out-of-sample forecast errors, adapting to serial dependence, volatility clustering, and distributional drift that invalidate classical conformal guarantees. Under H\"{o}lder- local stationarity and -mixing, we establish a four-term coverage-error decomposition and derive the optimal calibration window with coverage-error rate . A Le Cam two-point construction shows this rate is minimax-optimal over the H\"{o}lder- model class. The Bahadur representation is proved under both -mixing and the physical-dependence framework of Wu (2005). An oracle inequality formalises Winkler cross-validation as an adaptive window selector; the…
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