Evaluation of Missing Data Imputation for Time Series Without Ground Truth
This addresses a critical limitation for applications like 5G network management by enabling imputation evaluation without ground truth, though it is incremental as it builds on existing imputation techniques.
The paper tackled the problem of evaluating missing data imputation in time series without ground truth by introducing Wasserstein distance and Jensen-Shannon divergence as statistical metrics, showing they effectively assess imputation quality based on distribution alignment.
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.