Predicting City Transportation Vehicles Using GM(1,1) with Strengthening Buffer Operators

The smoothness of data sequence in time series forecasting has a significant impact on predictive accuracy. How to improve the modeling accuracy of a prediction model has become a very important academic issue. According to the theory of buffer operators, on the basis of the existing research results of strengthening buffer operators, we proposed a novel model building strengthening buffer operators. The problem that there are some contradictions between quantitative analysis and qualitative analysis in pretreatment for vibration data sequences is resolved effectively. An example demonstration shows that the new strengthening buffer operators presented in this paper can remarkably enhance the precision of GM(1,1)model.