Missing Data Imputation

Missing data imputation aims to fill in missing values in datasets, improving the accuracy and reliability of analyses. Current research focuses on developing sophisticated imputation methods using diverse model architectures, including generative adversarial networks (GANs), diffusion models, graph neural networks, and transformer networks, often incorporating iterative processes and leveraging techniques like Expectation-Maximization and self-attention mechanisms. These advancements are crucial for handling increasingly complex datasets across various fields, from biometrics and healthcare to network traffic analysis and renewable energy forecasting, where missing data is common and can significantly impact the validity of results. The development of more efficient and accurate imputation techniques is vital for ensuring reliable insights from incomplete data.