MATLAB Code Implementation for Grey Prediction Modeling

<p>Grey prediction models are well-suited for short-term forecasting applications, demonstrating superior performance within limited time horizons while potentially exhibiting some limitations in long-term predictions. The core algorithm involves data preprocessing through Accumulated Generating Operation (AGO) to reveal underlying patterns, followed by the construction of grey differential equations using GM(1,1) model formulation. Key MATLAB implementation steps include: initial sequence normalization, background value coefficient optimization, and parameter estimation via least squares method. Short-term forecasting remains critical across multiple industries and domains as it enables effective planning and data-driven decision-making. For enterprises, this approach provides rapid response capabilities to market fluctuations, allowing timely strategic adjustments to maintain competitive advantages. Although short-term prediction has inherent constraints, it serves as a valuable analytical tool when implemented with proper validation techniques such as residual testing and posterior variance checks to ensure model reliability.</p>