Anytime-Valid Detection of Scale-Regime Drift for Conformal Time-Series Forecasting

Conformal prediction intervals for time series can silently lose coverage when the data-generating process switches between scale regimes. Fixed calibration windows then underestimate uncertainty, while continuously adaptive methods often recover coverage only by substantially widening the intervals. We propose CREST (Conformal Recalibration via an Anytime-valid Scale-drift detector), a method that detects scale changes online and uses the resulting alarms to trigger conformal recalibration. CREST applies a blockwise e-process to seasonally whitened and locally standardized residuals, which yields anytime-valid false-alarm control under a local-scale null. To preserve this guarantee across repeated detector restarts, we introduce a budgeted restart scheme that allocates the false-alarm budget harmonically across segments. Experiments on real financial and weather time series show that CREST restores coverage close to the nominal level while maintaining interval widths comparable to a rolling baseline and requiring no trained state-space model. On car-index and weather data, CREST improves empirical coverage from 64.9% to 88.7% and from 77.1% to 89.8%, respectively, bringing both close to the nominal 90% target.