Imputation Method

Data imputation aims to fill in missing values in datasets, a crucial step for reliable data analysis and machine learning. Current research focuses on developing sophisticated imputation methods that leverage complex relationships within data, employing techniques like deep generative models (e.g., diffusion models, variational autoencoders), graph neural networks, and transformers, often incorporating temporal and spatial dependencies for time series data. These advancements improve the accuracy and robustness of imputation, particularly for high-dimensional and complex datasets, impacting various fields from healthcare and environmental science to network traffic analysis and recommendation systems. The ultimate goal is to minimize bias and uncertainty introduced by missing data, leading to more reliable insights and improved model performance.