Representative Period Selection

Representative period selection (RPS) focuses on identifying a smaller, representative subset of data that accurately reflects the characteristics of a larger dataset, crucial for efficient analysis and modeling across diverse fields. Current research emphasizes developing improved algorithms, including graph neural networks and autoencoders, to enhance the accuracy and efficiency of RPS, particularly when dealing with high-dimensional data or complex relationships within the data (e.g., graph structures). These advancements are significant for applications ranging from mitigating bias in large language models to optimizing power system planning and improving the representativeness of democratic bodies, ultimately leading to more robust and reliable results in various domains.