Time Series Imputation

Time series imputation aims to accurately fill in missing data points within sequential datasets, improving the reliability of subsequent analyses and predictions. Current research emphasizes probabilistic methods, leveraging architectures like recurrent neural networks, diffusion models, and graph neural networks to capture complex temporal and inter-feature dependencies, often incorporating uncertainty quantification. These advancements are crucial for various applications, from medical diagnosis using physiological signals to environmental monitoring and financial forecasting, where incomplete data is common and can significantly impact the accuracy and reliability of results. A growing focus is on developing generalizable models robust to diverse missing data patterns and capable of handling high-dimensional datasets.