用于临床多变量时间序列数据插补的时序高斯联结函数
Temporal Gaussian Copula For Clinical Multivariate Time Series Data Imputation
AI总结：
本文提出时序高斯Copula模型（TGC）用于三阶多变量时间序列插补，利用Gaussian Copula探索跨变量与时序关系，并结合EM算法提升不同缺失率下的鲁棒性，实验证明其优于现有方法。
摘要：
多变量时间序列（MTS）的插补尤为困难，因为MTS通常包含由仪器故障、无关数据干扰和隐私法规等多种因素导致的缺失值不规则模式。现有的统计方法和深度学习方法在时间序列插补中展现出可喜的成果。本文提出了一种用于三阶MTS插补的时序高斯Copula模型（TGC）。其核心思想是利用Gaussian Copula基于潜在高斯表示探索跨变量和时序关系。随后，我们采用期望最大化（EM）算法以提升在处理不同缺失率数据时的鲁棒性。在三个真实世界的MTS数据集上进行了全面实验。结果表明，我们的TGC显著优于最先进的插补方法。此外，TGC模型对测试数据集中不同的缺失比例表现出更强的鲁棒性。代码可在https://github.com/MVL-Lab/TGC-MTS获取。
英文摘要：
The imputation of the Multivariate time series (MTS) is particularly challenging since the MTS typically contains irregular patterns of missing values due to various factors such as instrument failures, interference from irrelevant data, and privacy regulations. Existing statistical methods and deep learning methods have shown promising results in time series imputation. In this paper, we propose a Temporal Gaussian Copula Model (TGC) for three-order MTS imputation. The key idea is to leverage the Gaussian Copula to explore the cross-variable and temporal relationships based on the latent Gaussian representation. Subsequently, we employ an Expectation-Maximization (EM) algorithm to improve robustness in managing data with varying missing rates. Comprehensive experiments were conducted on three real-world MTS datasets. The results demonstrate that our TGC substantially outperforms the state-of-the-art imputation methods. Additionally, the TGC model exhibits stronger robustness to the varying missing ratios in the test dataset. Our code is available at https://github.com/MVL-Lab/TGC-MTS.
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。
