Moving least squares is a statistical technique used for smoothing data and fitting curves to a set of points. It works by applying a weighted least squares approach, where nearby points have more influence on the fitted curve than those further away. This method is particularly useful in situations where data is noisy or irregularly spaced. The technique involves selecting a local neighborhood around each point and fitting a polynomial function to that subset of data. The weights assigned to each point in the neighborhood decrease with distance, allowing for a flexible and adaptive fit. Moving least squares is commonly used in fields like computer graphics, geostatistics, and machine learning.