Grey Accumulating Operators and Smoothing Operators

The grey prediction model studies the prediction modeling problems involving “small samples and poor information”, with a minimum modeling sample of only four sets of data. The biggest difference between grey prediction models and regression prediction models lies in their requirement for the sample size in modeling. Based on mathematical statistics in theory, a regression prediction model establishes the functional relationship between dependent and independent variables by exploring the statistical laws between them, which requires a large sample size (at least 30 sets of data).