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基于ARFIMA模型的钢材价格预测研究
引用本文:陈雪,申建红,徐文慧,朱琛.基于ARFIMA模型的钢材价格预测研究[J].河北工程大学学报,2020,37(3):64-68.
作者姓名:陈雪  申建红  徐文慧  朱琛
作者单位:青岛理工大学 管理工程学院,山东 青岛266520,青岛理工大学 管理工程学院,山东 青岛266520;山东省高校智慧城市建设管理研究中心,山东 青岛266520,青岛理工大学 管理工程学院,山东 青岛266520,青岛理工大学 管理工程学院,山东 青岛266520
基金项目:国家自然科学基金资助项目(71471094)
摘    要:钢材价格的准确预测有利于施工企业拟定合理的材料采购策略。针对当前钢材价格的预测研究中均未考虑其价格变动的长记忆性,导致建模过程中有效信息丢失,预测误差增大。建立了考虑长记忆性的ARFIMA钢材价格预测模型,以青岛市2014年1月到2019年6月螺纹钢的价格为研究对象进行了钢材价格预测,并利用ARFIMA模型和ARIMA模型的预测值与真实值进行对比分析,实验结果显示:ARFIMA模型较ARIMA模型的钢材价格预测精准度提高了1.7%,且预测效果更稳定。

关 键 词:钢材价格预测  ARFIMA模型  时间序列  长记忆性
收稿时间:2020/4/10 0:00:00

Research on Steel Price Forecasting Based on ARFIMA Model
Authors:CHEN Xue  SHEN Jianhong  XU Wenhui  ZHU Chen
Affiliation:School of Management Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China,School of Management Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China;University Research Center for Smart City Construction and Management of Shandong Province, Qingdao, Shandong 266520, China,School of Management Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China and School of Management Engineering, Qingdao University of Technology, Qingdao, Shandong 266520, China
Abstract:The accurate prediction of steel prices is helpful for construction companies to formulate reasonable material procurement strategies. For the current steel price prediction research, the long memory of its price changes is not considered, resulting in the loss of effective information in the modeling process and the increase of prediction errors. In this paper, the ARFIMA steel price prediction model considering long memory was established. The steel price prediction was carried out based on the price of rebar in Qingdao from January 2014 to June 2019. The predicted values of the ARFIMA model and ARIMA model were used for comparative analysis of the true value. The experimental results show that the accuracy of the steel price prediction of the ARFIMA model is 1.7% higher than that of the ARIMA model, and the prediction effect is more stable.
Keywords:steel price forecast  ARFIMA model  time series  long memory
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