A damping grey multivariable model and its application in online public opinion prediction

Online public opinion plays pivotal role in social stability, and predicting hotness of online opinion accurately can provide theoretical and practical guidance for government and enterprises. A damping accumulated multivariable grey model is proposed to forecast the online public opinion trends in this paper. Firstly, the dynamic damping trend factor is introduced into the accumulation process, so that the model can adjust the accumulating order of different sequences more flexibly. Secondly, considering that the accumulated sequences have grey exponential rate property, the damping grey multivariable model is established by optimizing the structure of the background values. Finally, due to the assumption that the relevant factor variables are grey constants, the systematic error occurs in the traditional grey multivariate model, the time response equation is given to reduce error by using the composite quadrature method. Two real cases are used for empirical analysis to verify the effectiveness of the new model. And the forecasting accuracy and robustness of the new model is better than those of other prediction models. Therefore, the model is an effective method dealing with nonlinear problems, which further improves the grey modeling theory and can be applied to the prediction of online opinion. •A novel damping background optimization multivariable grey model (DBOGM(1,N)) is proposed.•The dynamic damping trend factor is introduced into the accumulation process.•The structure of the background values of DBOGM(1,N) model is reconstructed.•The time response equation is given by using the composite quadrature method.•The systematic error due to the assumption that relevant variables are grey constants is reduced.