# Evaluation of Missing Data Imputation for Time Series Without Ground Truth

arXiv: https://arxiv.org/abs/2503.05775 · HTML: https://mlsanity.com/paper/2503.05775/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 1 of 1)

- [LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal Patterns](https://mlsanity.com/paper/1909.04293/) (2019)

## Built on by (most cited first, 2 of 2)

- [STDiff: A State Transition Diffusion Framework for Time Series Imputation in Industrial Systems](https://mlsanity.com/paper/2508.19011/) (2025)
- [RFX-Fuse: Breiman and Cutler's Unified ML Engine + Native Explainable Similarity](https://mlsanity.com/paper/2603.13234/) (2026)

## Abstract (arXiv)

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.