Grey differential dynamic multivariate forecasting model and its application

To solve the shortcoming of data-driven modeling method in characterizing system characteristics, a grey differential dynamic multivariable prediction model was proposed. In the new model, the behavior sequence and influence sequence with system characteristics are used to model, which strengthens the system dynamics and nonlinearity. At the same time, the least square principle is used to obtain the parameter estimation, and Laplace transform is used to derive the approximate time response. On this basis, the model was applied to carbon emissions projections for three typical regions: The European Monetary Union, the Middle East and North Africa, and sub-Saharan Africa. Application examples show that prediction effect of grey differential dynamic multivariable prediction model is better than other three classic grey prediction model, can effectively predict the carbon emissions of three regional in the next five years. Meanwhile, it shows that the new model can better describe the dynamic problem of multi-factor system, so as to effectively improve the modeling accuracy of the traditional grey multivariable model. © 2022, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.