MATLAB Implementation of Grey Prediction Models

This documentation presents a comprehensive MATLAB implementation of grey prediction models. The provided code is fully functional and debugged - users simply need to input their data to generate predictions. The implementation includes key algorithms such as GM(1,1) and GM(2,1) models, with built-in functions for data preprocessing, model parameter estimation, and accuracy validation. To maximize the utility of this program, we recommend studying both the theoretical foundations and practical applications of grey prediction systems. You may explore different model variants to understand their respective advantages and limitations in handling various data patterns. The code architecture allows for parameter optimization through techniques like residual analysis and posterior variance testing to enhance prediction precision. Furthermore, the modular design facilitates adaptation to diverse domains including economics, environmental science, and medical research. Through deeper investigation of these aspects, users can leverage the program's full potential to develop more accurate forecasts and achieve superior practical outcomes.