With the development of big data and artificial intelligence,constructing a reasonable engineering cost index is an inevitable trend in the development of engineering costs.Research is based on the combination of grey prediction model and Gradient Boosted Decision Tree(GBDT)prediction model,combined with the Stacking strategy to obtain the GM-GBDT engineering cost index combination prediction model.Analyzing the performance of the models,it was found that among the three models,the highest to lowest predictive performance was the GM-GBDT integrated prediction model,GBDT prediction model,and GM(1,N)prediction model;The relative error between the actual and predicted values of the engineering cost index from January to December 2020 using the GM-GBDT integrated prediction model is 3.86%-1.05%,with an average relative error of 2.60%.The empirical analysis results indicate that the GM-GBDT joint model has better overall prediction ability and can further improve prediction accuracy on the basis of GM(1,N)and GM.
维普
万方数据