Spatiotemporal Imputation

Spatiotemporal imputation focuses on reconstructing missing values in data exhibiting both spatial and temporal dependencies, a common problem across diverse fields like environmental monitoring and transportation. Current research emphasizes the development of sophisticated deep learning models, including transformers, graph neural networks, and diffusion models, often incorporating techniques like attention mechanisms and contrastive learning to effectively capture complex spatiotemporal relationships. These advancements aim to improve the accuracy and generalizability of imputation methods, enabling more reliable analysis and prediction from incomplete datasets. The impact extends to various applications, improving the quality of data-driven insights and decision-making in numerous scientific and engineering domains.