Grey Prediction Model

This text introduces "small sample development forecasting," a significant topic worthy of further expansion. Grey prediction modeling serves as an effective methodology for forecasting future trends with limited data samples. This approach finds extensive applications not only in economics but also across diverse fields such as medicine and social sciences. In these domains, small sample forecasting enables decision-makers to better comprehend current conditions and anticipate future trajectories. Thus, grey prediction represents both a practical technique and a conceptual framework for understanding and projecting developmental directions. The core algorithm typically involves accumulating generation operations and differential equation modeling, where key functions like GM(1,1) implement data sequence transformations through cumulative sum operations and parameter estimation via least squares methods. Implementation typically requires matrix operations for coefficient calculation and iterative computations for prediction sequence generation, making it particularly suitable for systems with incomplete information.