MATLAB Implementation of Gray Prediction Model

This program is a MATLAB implementation of the gray prediction model, primarily designed for time series forecasting. Gray prediction is a small-sample-based modeling technique that enables data prediction, analysis, and decision-making without requiring large datasets. The implementation includes key algorithmic components such as accumulated generating operation (AGO) to reduce data randomness, establishment of gray differential equations using least squares method, and inverse accumulated generating operation (IAGO) for prediction restoration. When using this program, you can adjust parameters and data preprocessing methods according to your specific requirements to optimize prediction performance. The code structure allows modification of critical functions including data normalization, GM(1,1) model building, and precision validation through posterior variance tests. Additionally, you can further explore the gray prediction model's theoretical foundations and MATLAB implementation details to enhance your understanding and application of this forecasting methodology.