Improved Algorithm for Residual Grey Model

This article explores the residual grey model and its enhanced algorithm, an effective forecasting method with broad practical applications. We present several improvement techniques to boost prediction accuracy and stability, including parameter optimization methods, diverse data processing approaches, and integration with machine learning algorithms. The implementation typically involves key functions for residual correction and grey differential equation solving, where developers can adjust weighting factors and smoothing parameters through configuration files. Additionally, we provide comprehensive test files containing sample datasets and validation scripts that demonstrate the model's performance across different scenarios. These test cases include benchmark comparisons and error analysis routines, allowing users to evaluate prediction accuracy using metrics like Mean Absolute Percentage Error (MAPE). Through these enhancements, the residual grey model evolves into a more robust and reliable forecasting tool suitable for industrial applications and research purposes.