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

Abstract

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.

Forecasting the Number of the Wounded after an Earthquake Disaster Based on the Continuous Interval Grey Discrete Verhulst Model

“…[19]. e traditional discrete GM (1,1) model does not always fit well, and sometimes the prediction error is large [20,21], so some scholars began to study interval grey numbers. Zeng et al developed a DGM (1,1) prediction model for interval grey number series [22].…”

Future of renewable energy consumption in France, Germany, Italy, Spain, Turkey and UK by 2030 using optimized fractional nonlinear grey Bernoulli model

“…The simplest form of grey prediction models is GM(1,1), firstly proposed by Deng ( Deng, 1982 ). The main advantage of this model is that it can be used to predict a small number of sequence data ( Liu et al., 2016 ). The methodology of GM(1,1) is based on the first-order single variable prediction ( Ma et al., 2013 ) and this method was used in forecasting of energy by researchers ( Yuan et al., 2016 ; Tsai, 2016 ; Li and Li, 2017 ; Şahin, 2018 ).…”

Modified Hybrid Grey Model (1,1) to Forecast Cellular Subscribers

“…Huang CY, LU CY and Chen Cl this work is on GM (1,1) and nonlinear GBM and it is suggested that GM (1,1) is good for slow rate of change of data whereas nonlinear GBM (1,1) is good for drastically rate of change of data [9]. Ersi Liu, Qiangqiang Wang, Xinran Ge and Wei Zhou has shown two models that is the concave and convex DDGM (1,1) in these models equal division numbers are used by using two kind of approaches first is gradually heuristics method and other one is dichotomy method and eventually through conflict events in the urbanization process in china it was proved that DDGM (1,1) has higher accuracy than Discrete, optimal and GM (1,1) [10]. In another work that was put to forecast the growth rate of renewable energy consumption in China, in this work three model that is GM (1,1), nonlinear grey Bernoulli (1,1) and grey verhulst model is compared with each other and it can be show that grey verhulst model has greater accuracy than the two model the accuracy and fitness of models are also compared by regression analysis [11].…”