置信填充：一个不确定性感知的多变量时间序列填充框架
Impute With Confidence: A Framework for Uncertainty Aware Multivariate Time Series Imputation
AI总结：
针对时间序列填充中模型不确定性常被忽略的问题，提出一个量化并利用不确定性进行选择性填充的通用框架，实验证明该方法能降低填充误差并提升下游预测任务性能。
摘要：
包含缺失值的时间序列数据在许多领域都很常见。由于传感器断开连接的时间较长，医疗保健领域带来了特殊挑战。在这种情况下，为填充值提供置信度度量至关重要。现有的大多数方法要么忽略了模型不确定性，要么缺乏估计不确定性的机制。为了填补这一空白，我们引入了一个通用框架，用于量化和利用不确定性进行选择性填充。通过关注模型最有信心的值，避免了高度不可靠的填充。我们在多个 EHR 数据集上进行了实验，涵盖了各种类型的缺失情况，结果表明，选择性地填充不确定性较低的值不仅可以减少填充误差，还能改善下游任务。具体而言，我们在 24 小时死亡率预测任务中展示了性能提升，强调了将不确定性纳入时间序列填充的实际益处。
英文摘要：
Time series data with missing values is common across many domains. Healthcare presents special challenges due to prolonged periods of sensor disconnection. In such cases, having a confidence measure for imputed values is critical. Most existing methods either overlook model uncertainty or lack mechanisms to estimate it. To address this gap, we introduce a general framework that quantifies and leverages uncertainty for selective imputation. By focusing on values the model is most confident in, highly unreliable imputations are avoided. Our experiments on multiple EHR datasets, covering diverse types of missingness, demonstrate that selectively imputing less-uncertain values not only reduces imputation errors but also improves downstream tasks. Specifically, we show performance gains in a 24-hour mortality prediction task, underscoring the practical benefit of incorporating uncertainty into time series imputation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
