Simple Implementation Method for Grey Prediction

When implementing grey prediction, the following simple method can be adopted. First, input the parameter N specifying the number of values to be predicted. Then, extract the required one-dimensional input array 'data' and perform necessary data transformation and preprocessing steps, typically including data normalization and cumulative generation operations. Subsequently, apply a series of grey prediction models and methods, such as grey relational analysis and the fundamental GM(1,1) grey prediction model. These methods enable data forecasting through mathematical modeling, where the GM(1,1) model specifically involves constructing a first-order differential equation based on accumulated generating operations (AGO) to establish prediction patterns. The implementation typically includes steps like establishing grey differential equations, parameter estimation using least squares method, and model accuracy verification through posterior variance tests. Therefore, when conducting grey prediction, particular attention must be paid to the characteristics and applicable scope of various prediction methods. Simultaneously, necessary data processing and transformation must be performed to ensure the accuracy and reliability of prediction results. This includes data quality checks, handling of missing values, and appropriate model selection based on data characteristics. Through comparative analysis of results from different prediction methods and practical adjustments based on actual scenarios, relatively accurate prediction outcomes can be ultimately derived. The implementation should include validation mechanisms such as residual analysis and model fitness evaluation to optimize prediction performance.