🤖 AI Summary
This work addresses the challenge of conformal prediction under distributional shifts and structural changes in non-stationary streaming data, where traditional methods relying on exchangeability assumptions often fail. To overcome this limitation, the authors propose the DASC framework, which uniquely integrates local spectral similarity with an optimal transport–based drift score to dynamically weight residuals and adaptively adjust both the calibration pool and the target miscoverage level. This approach enables adaptive uncertainty quantification for non-exchangeable streaming data and introduces an online effective sample size diagnostic to assess predictive robustness. Experimental results on synthetic and real-world datasets—including electricity load, weather, and financial time series—demonstrate that DASC consistently achieves nominal or conservative coverage while reducing average prediction interval width by 28%–42% compared to existing methods.
📝 Abstract
Conformal prediction provides distribution-free prediction intervals under exchangeability, but many modern data streams are neither independent nor stable. They exhibit recurring regimes, changing seasonal frequencies, abrupt shifts, and gradual drift. We propose drift-aware spectral conformal prediction (DASC), a streaming uncertainty quantification framework for structured non-exchangeable data subject to distributional drift. DASC forms conformal prediction intervals using calibration residuals weighted by local spectral similarity, while a transport-based drift score monitors whether the current test distribution has moved away from past calibration regimes. When drift is mild, DASC borrows calibration residuals from structurally similar historical windows; when drift is severe, it contracts or reweights the calibration pool and updates the target miscoverage level online. The method also reports an effective sample size diagnostic that warns when a weighted conformal quantile is statistically fragile. We establish an approximate coverage bound that decomposes coverage loss into drift, residual mismatch, and weighted effective sample size. In synthetic experiments and five stress-test regimes, DASC maintains near-nominal coverage after drift where rolling, recency-weighted, and spectral-only conformal methods can under-cover. In real electricity and weather streams, DASC reduces average interval width by approximately 28% and 42%, respectively, relative to the best calibrated non-DASC baseline, while preserving calibrated or conservative coverage. A financial volatility example shows a more nuanced regime in which spectral-only calibration is competitive, but DASC retains near-nominal coverage and adds drift diagnostics.