Title: Evaluation of Missing Data Imputation for Time Series Without Ground Truth Show Chinese title

Title: 时间序列缺失数据填补的评估

Abstract: The challenge of handling missing data in time series is critical for maintaining the accuracy and reliability of machine learning (ML) models in applications like fifth generation mobile communication (5G) network management. Traditional methods for validating imputation rely on ground truth data, which is inherently unavailable. This paper addresses this limitation by introducing two statistical metrics, the wasserstein distance (WD) and jensen-shannon divergence (JSD), to evaluate imputation quality without requiring ground truth. These metrics assess the alignment between the distributions of imputed and original data, providing a robust method for evaluating imputation performance based on internal structure and data consistency. We apply and test these metrics across several imputation techniques. Results demonstrate that WD and JSD are effective metrics for assessing the quality of missing data imputation, particularly in scenarios where ground truth data is unavailable. Show Chinese abstract

Abstract: 处理时间序列中缺失数据的挑战对于在第五代移动通信（5G）网络管理等应用中保持机器学习（ML）模型的准确性和可靠性至关重要。 传统的插补验证方法依赖于真实数据，而真实数据本质上是不可用的。 本文通过引入两个统计指标——Wasserstein距离（WD）和Jensen-Shannon散度（JSD），解决了这一限制，以在不需要真实数据的情况下评估插补质量。 这些指标评估插补数据与原始数据分布之间的对齐程度，提供了一种基于内部结构和数据一致性的稳健插补性能评估方法。 我们在多种插补技术上应用并测试了这些指标。 结果表明，WD和JSD是评估缺失数据插补质量的有效指标，特别是在真实数据不可用的场景中。