用高斯对偶模型提升多变量时间序列缺失值填补精度与鲁棒性
Temporal Gaussian Copula For Clinical Multivariate Time Series Data Imputation
- 基于潜变量高斯对偶建模变量间与时间依赖关系
- 在三个真实数据集上超越现有方法，尤其在不同缺失率下表现更稳
- 适合医疗时间序列分析、临床数据修复等场景
多变量时间序列（MTS）的填补极具挑战性，因缺失模式不规则，常由设备故障、无关数据干扰或隐私政策导致。现有统计与深度学习方法虽有进展，但尚存不足。本文提出时序高斯对偶模型（TGC），用于三阶多变量时间序列填补。核心思想是利用高斯对偶捕捉变量间与时间上的潜在关联，通过期望最大化（EM）算法增强对不同缺失率数据的鲁棒性。在三个真实世界MTS数据集上进行综合实验，结果表明TGC显著优于当前最优填补方法，且在测试集不同缺失率下仍具更强稳定性。代码已开源：https://github.com/MVL-Lab/TGC-MTS。
原文摘要 · Abstract (English)
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.
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