Open AccessResearch on demand forecast of vehicle turnover equipment based on GM(1,1)-BP combined model
Author(s) -
David Zhang,
Fengju Li,
Fengzhong Wang,
Xie Xin-peng
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/688/5/055008
Subject(s) - residual , demand forecasting , computer science , artificial neural network , econometrics , operations research , engineering , artificial intelligence , economics , algorithm
According to the historical data characteristics of vehicle turnover equipment demand, a GM (1,1) - BP combined model is established. Firstly, GM (1,1) model is used to forecast the historical data of vehicle turnover equipment demand. On this basis, BP neural network is introduced to correct the residual of the prediction. It optimizes the forecasting method of vehicle turnover equipment demand, makes up the deficiency of single model, and enhances the accuracy of vehicle turnover equipment demand forecasting.