标题:A combined model based on GM and SARIMA: An example of excavator demand forecasting
作者:Zhao, Jing ;Wang, Zhaohui ;Zhang, Zhongge ;Han, Yunpeng
作者机构:[Zhao, Jing ;Wang, Zhaohui ;Zhang, Zhongge ;Han, Yunpeng ] School of Mechanical Engineering, Shandong University, Jinan, China
会议名称:4th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
会议日期:12 April 2019 through 15 April 2019
来源:2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
出版年:2019
页码:232-236
DOI:10.1109/ICCCBDA.2019.8725752
关键词:DE; excavator demand; forecasting; GM(1,1) modle; SARIMA model
摘要:Accurate excavator demand forecasts not only improve the service level of the manufacturer, but also reduce the bullwhip effect throughout the supply chain. In this paper, a new combined forecasting method based on Gray Model (GM) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed. In the excavator market, noise signals usually affect prediction accuracy, which were caused by different instability factors. First, two univariate models, SARIMA model and GM (1, 1) model, are used to forecast excavator demand. The precision of the two models are in line with requirements, but the residues of the two models are opposite in a statistical sense. Then a combined model is constructed with these two models, the Differential Evolution Algorithm (DE) is used to optimize the weight coefficients of the two prediction methods of GM and SARIMA. Through comparing, it is found that the fitted values of combined model respond less to the fluctuations and its MAPE (Mean Absolute Percent Error) is smaller than SARIMA model and GM (1, 1) model. And then, China's excavators demand is forecasted by using the three models. © 2019 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067496922&doi=10.1109%2fICCCBDA.2019.8725752&partnerID=40&md5=f7f5c38f5b08ca63918a2643ee3ca018
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