Dynamic Discrete GM (1,1) Model and Its Application in the Prediction of Urbanization Conflict Events

In the empirical researches, the discrete GM (1,1) model is not always fitted well, and sometimes the forecasting error is large. In order to solve this issue, this study proposes a dynamic discrete GM (1,1) model based on the grey prediction theory and the GM (1,1) model. In this paper, we use the equal division technology to fit the concavity and convexity of the cumulative sequence and then construct two dynamic average values. Based on the dynamic average values, we further develop two dynamic discrete GM (1,1) models and provide the gradual heuristics method to draw the initial equal division number and the dichotomy approach to optimize the equal division number. Finally, based on an empirical analysis of the number of conflict events in the urbanization process in China, we verify that the dynamic discrete GM (1,1) model has higher fitting and prediction accuracy than the GM (1,1) model and the discrete GM (1,1) model, and its prediction result is beneficial to the government for prevention and solution of the urbanization conflict events.