A prediction method using the grey model GMC(1, n) combined with the grey relational analysis: a case study on Internet access population forecast

Prediction is important for the modern scientific management. Accurate prediction can help the policymaker make correct decision and promote the decision-making quality. The paper proposed an integrated prediction method using the grey model GMC(1, n) combined with the improved grey relational analysis. The GMC(1, n) model, obtained by integrating the convolution technology in the GM(1, n) model to establish the exact solution of grey model, greatly enhances the applicability of latter. The effects of time lag are also included in the study. The improved grey relational analysis considers the consistency of two factors not only in “magnitude”, as the traditional grey relational method did, but also “direction”. The proposed combined method provides tremendous improvement over the existing method. At the end, the application of the proposed the grey prediction model GMC(1, n) has high accuracy of prediction and Taiwan's Internet access population will be forecasted in this article by GMC(1, n) combined with the grey relational grade analysis.