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作为钢铁工业生产的重要原材料之一,铁矿石进口价格的剧烈波动给我国钢铁企业带来巨大的冲击.本文通过分析影响铁矿石价格波动的多种因素,包括供需关系、运费成本、国内外经济环境等,挖掘影响铁矿石进口价格的关键因素,综合考虑其线性和非线性均有的复杂时间序列特征,提出一种基于误差修正模型(error correction model,ECM)和支持向量回归(support vector regression,SVR)的铁矿石价格混合预测模型ECM-SVR.实证结果表明:与单一基准模型和传统混合模型相比,新模型具有较高的预测准确率,这对于钢铁企业控制原料成本和市场投资者合理规避价格风险具有重要指导作用.
As one of the important raw materials in the iron and steel industry, the drastic fluctuation of the import price of iron ore has brought tremendous impact on the iron and steel enterprises in our country.Through analyzing many factors that affect the price fluctuation of the iron ore, including supply and demand, freight cost, Foreign economic environment and so on, mining the key factors affecting the import price of iron ore and considering the complex time series features both of linearity and non-linearity, an error correction model (ECM) and support vector regression support vector regression (SVR). The empirical results show that the new model has a higher prediction accuracy than the single reference model and the traditional mixed model, which is of great value to the iron and steel enterprises in controlling the cost of raw materials And market investors reasonable to avoid price risk has an important guiding role.