Sliding Window

Sliding window techniques process sequential data by analyzing a fixed-size segment ("window") that moves through the data stream, offering a balance between capturing temporal context and computational efficiency. Current research focuses on improving sliding window algorithms across diverse applications, including frequency estimation, recommender systems, and dynamic functional connectivity analysis, often employing machine learning models (e.g., transformers, graph neural networks) or novel algorithms (e.g., random convolutions) to enhance accuracy and efficiency. These advancements are significant for handling large-scale, dynamic data streams in various fields, from real-time data analysis to machine learning model training and deployment, improving both accuracy and computational performance.