Conformal Prediction Intervals with Temporal Dependence
存在时间依赖时的保形预测区间AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究时间序列回归中的预测区间与时间依赖，对应时间序列及预测方法。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
存在时间依赖时的保形预测区间
- 研究问题
- 对具有多个个体的时间序列回归，如何区分并兼顾横截面覆盖与纵向覆盖，构造具有明确有效性含义的预测区间？
- 方法与思路
- 作者提出利用校准集的事后保形预测程序 CPTD，在保持严格横截面有效性的同时改善纵向覆盖，并结合神经网络实验。
- 材料陈述的贡献
- 摘要称证明分布无关的纵向有效性不可能实现；所提程序在多个数据集上改善纵向覆盖，且经常给出更窄区间。
- 阅读关注点
- 应核对两种覆盖率的概率定义、不可能性结论的适用范围、横截面保证所需条件，以及时间依赖如何进入校准程序。
- 阅读边界
- 这是 arXiv 预印本，不能确认同行评审或接收。摘要未说明完整依赖条件，纵向覆盖改善也不等于严格的纵向有效性保证。
原文摘要
Cross-sectional prediction is common in many domains such as healthcare, including forecasting tasks using electronic health records, where different patients form a cross-section. We focus on the task of constructing valid prediction intervals (PIs) in time series regression with a cross-section. A prediction interval is considered valid if it covers the true response with (a pre-specified) high probability. We first distinguish between two notions of validity in such a setting: cross-sectional and longitudinal. Cross-sectional validity is concerned with validity across the cross-section of the time series data, while longitudinal validity accounts for the temporal dimension. Coverage guarantees along both these dimensions are ideally desirable; however, we show that distribution-free longitudinal validity is theoretically impossible. Despite this limitation, we propose Conformal Prediction with Temporal Dependence (CPTD), a procedure that is able to maintain strict cross-sectional validity while improving longitudinal coverage. CPTD is post-hoc and light-weight, and can easily be used in conjunction with any prediction model as long as a calibration set is available. We focus on neural networks due to their ability to model complicated data such as diagnosis codes for time series regression, and perform extensive experimental validation to verify the efficacy of our approach. We find that CPTD outperforms baselines on a variety of datasets by improving longitudinal coverage and often providing more efficient (narrower) PIs.