学术论文

      基于主成分回归分析法预测中国铁矿石需求

      Prediction of demand for iron ores in China based on principal component regression analysis

      摘要:
      在论述铁矿石需求预测途径的基础上,选取影响我国铁矿石需求的8个基本因素,采用回归分析方法进行了铁矿石需求的单因素分析.单因素分析结果表明,选取的8个基本因素与铁矿石需求的相关度基本都大于0.9.对8个基本影响因素进行了主成分分析,最终降维为4个主成分.将主成分分析方法与回归分析方法相结合,建立了铁矿石的需求预测模型,并对我国2015年和2020年铁矿石的需求量进行了预测,分别为29.76亿t和26.68亿t.
      Abstract:
      Based on predicted methods of demand for iron ores,eight basic factors influencing the demand for iron ores in China were selected for single factor regressing analysis.The results show that the degree of correlation between the eight basic factors and demand for iron ores is more than 0.9.The principal component analysis method was used to analyze the relationships among the eight basic factors and four principal components were determined among the eight basic factors.Combined the principal component analysis method with the regressing analysis method,a prediction model of demand for iron ores was established.Using the model,the demands for iron ores in 2015 and 2020 in China were predicted and their values are 29.76 billion tons and 26.68 billion tons,respectively.
      作者: 牛京考
      作者单位: 北京科技大学土木与环境工程学院,北京100083/中国钢铁工业协会冶金科技发展中心,北京100711
      刊 名: 北京科技大学学报 ISTICEIPKU
      年,卷(期): 2011, 33(10)
      分类号: TD98
      机标分类号: TM7 F40
      在线出版日期: 2012年4月21日
      基金项目: “十一五”国家科技支撑计划资助项目