Prediction of demand for iron ores in China based on principal component regression analysis
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摘要: 在論述鐵礦石需求預測途徑的基礎上,選取影響我國鐵礦石需求的8個基本因素,采用回歸分析方法進行了鐵礦石需求的單因素分析.單因素分析結果表明,選取的8個基本因素與鐵礦石需求的相關度基本都大于0.9.對8個基本影響因素進行了主成分分析,最終降維為4個主成分.將主成分分析方法與回歸分析方法相結合,建立了鐵礦石的需求預測模型,并對我國2015年和2020年鐵礦石的需求量進行了預測,分別為29.76億t和26.68億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.
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Key words:
- iron ores /
- demand forecasting /
- mathematical model /
- principal component analysis /
- regression analysis
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