Drift-Aware Spectral Conformal Prediction for Non-Exchangeable Streaming Data
面向非可交换流数据的漂移感知谱共形预测AI 解读 ↓
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面向非可交换流数据的漂移感知谱共形预测
- 研究问题
- 当流数据存在周期变化、突变与渐进漂移时，如何利用历史校准信息构造预测区间，并识别覆盖率失稳的风险？
- 方法与思路
- 作者按局部谱相似性加权校准残差，以运输型指标监测漂移，动态调整校准池和误覆盖目标，并报告有效样本量。
- 材料陈述的贡献
- 摘要称，覆盖损失可分解为漂移、残差失配与有效样本量因素；部分实验保持近目标覆盖，并缩窄预测区间。
- 阅读关注点
- 应核对近似覆盖界的条件、漂移指标定义、在线误覆盖目标的变化，以及区间宽度比较是否对应相同覆盖要求。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列覆盖界条件，金融例子也不支持所有情境下均占优。
原文摘要
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.