👉 Window math, also known as windowed math or sliding window operations, is a technique used in data analysis and machine learning to process data over finite, moving segments or windows rather than the entire dataset at once. This approach allows for the extraction of local patterns, trends, or features that might be obscured in the broader context. For example, in time series analysis, window math can help identify short-term trends or seasonal patterns by analyzing data points within a specific time frame. Similarly, in image processing, it can be used to detect edges or textures by examining pixel values within small, overlapping regions. By focusing on these localized segments, window math provides a more nuanced and context-aware analysis compared to global methods.
