👉 Sliding computation, or sliding window computation, is a technique used in machine learning and data analysis to process sequential data by examining overlapping subsets of the data points. This method involves defining a window or "sliding" frame that moves across the dataset, computing functions or models on each window, and then aggregating or analyzing the results. Sliding windows are particularly useful for tasks like time series analysis, natural language processing, and sequence prediction, where the context or relationship between data points changes over time. By focusing on local patterns and trends within each window, sliding computation allows for efficient and effective modeling of dynamic data without needing to consider the entire dataset at once, making it a versatile tool in handling large and complex datasets.
