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基于ARIMA的油田A类物资市场价格预测
引用本文:王春宝,刘若阳.基于ARIMA的油田A类物资市场价格预测[J].运筹与管理,2013,22(5):160-165.
作者姓名:王春宝  刘若阳
作者单位:1.大庆油田物资集团,黑龙江 大庆 163114;2.中国科学院 数学与系统科学研究院,北京 100190
基金项目:大庆油田物资集团项目(dqc-2010-xdgl-ky-002);中国科学院管理、决策与信息系统重点实验室资助项目
摘    要:本文研究了油田A类物资的市场价格预测。采用时间序列方法中的ARIMA模型,结合油田物资历史价格数据,分析并提出了一套针对油田A类物资的市场价格预测模式。该模式包括两个模块:样本集模块和ARIMA模块。样本集模块的主要功能是样本集的输入和实时更新;ARIMA模块包括了价格预测模型的拟合、检验、预测、评价、动态反馈和调整等主要环节。在该模式的指导下,以大庆油田A类采购物资中的小螺纹钢(20-HRB335)(天津、石家庄和沈阳3个产地)为例,对2011年各月的市场价格进行了模拟预测,预测的平均相对误差分别控制在2.13%,1.64%和1.82%,该结果得到了用户的认可。该预测模式的运用对大庆油田物资集团制定合理的物资采购方案提供了依据。结论部分对该预测模式的意义及存在问题进行了分析,并给出改进建议。

关 键 词:应用数学  市场价格预测  时间序列方法  ARIMA模型  大庆油田A类物资  
收稿时间:2012-05-29

Study on Market Price of Oilfield Class-A Materials Forecasting Based on ARIMA Model
WANG Chun-bao,LIU Ruo-yang.Study on Market Price of Oilfield Class-A Materials Forecasting Based on ARIMA Model[J].Operations Research and Management Science,2013,22(5):160-165.
Authors:WANG Chun-bao  LIU Ruo-yang
Institution:1. Daqing Oilfield Materials Corporation, Daqing 163114, China;2. Academy of Mathematics and Systems Science, CAS, Beijing 100080, China
Abstract:The monthly market-price forecasting framework for the Oilfield Class-A materials, taking a large proportion in total purchasing cost, is considered based on the ARIMA model in time series method. The framework includes the sample set module and the ARIMA module. The sample set module provides a sample input for forecasting and updating in real time. The ARIMA module includes how to fit, test, forecast, evaluate and dynamically revise the model. According to the framework, in China Daqing Oilfield, market-prices in three places from January to December in 2011 of the Small Deformed Steel Bar(20-HRB335)in Class-A materials are predicted, including Tianjin, Shijiazhuang and Shenyang. The accuracies of the prediction are no more than 2.13%,1.64% and 1.82%, respectively, which is given a high evaluation by users. It provides the basis for Daqing Oilfiled Materials Corporation in making optimal material purchasing decision. Finally, suggestions of improving this framework are presented.
Keywords:applied mathematics  market price forecasting  time series method  ARIMA model  class-A oilfield materials of Daqing  
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