Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
留出一个窗口：用于时间序列预测推断的 Jackknife 修正AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究时间依赖下的预测区间、覆盖保证及时间序列预测推断。
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留出一个窗口：用于时间序列预测推断的 Jackknife 修正
- 研究问题
- 论文研究时间依赖和非对称训练如何损害普通留一法预测区间覆盖，以及如何避免样本切分造成的精度损失并恢复有效覆盖。
- 方法与思路
- 作者提出留窗口法，以模型拟合稳定性和偏离循环可交换性的系数量化覆盖误差，并通过时间序列实验比较预测区间表现。
- 材料陈述的贡献
- 摘要称轻度依赖也可使普通留一法覆盖任意恶化，而留窗口法在稳定性条件下有效；实验中常较切分保形方法给出更窄区间。
- 阅读关注点
- 应核对窗口宽度选择、稳定性的精确定义、循环可交换性偏离系数，以及覆盖保证对应的样本规模、依赖程度和训练程序。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未给出覆盖误差界和完整条件，较窄区间的实验结论不保证普遍成立。
原文摘要
Conformal prediction methods enjoy strong theoretical and empirical predictive inference performance, provided the data is exchangeable and is treated symmetrically during training. However, these assumptions are impractical in many settings, such as time series, where temporal dependence violates exchangeability and it is preferable to use predictors that leverage dependence by treating data asymmetrically. Recent work shows that split conformal prediction is robust to these issues, but sample splitting can reduce accuracy, motivating the study of methods that do not rely on data splitting in the time series setting. In this work, we show that the vanilla leave-one-out jackknife can suffer arbitrary loss of coverage even in canonical time series models with mild temporal dependence. As a remedy, we propose a modification tailored to such settings, which we term the leave-a-window-out (LWO) method, and show that it can achieve valid coverage provided that the model-fitting procedure satisfies mild stability properties. Our proofs are based on quantifying the degree to which the data departs from cyclic exchangeability, which we introduce new coefficients to measure. Experiments on time series demonstrate that our method often enjoys valid coverage when the vanilla jackknife fails to cover, while producing much narrower intervals than split conformal prediction.