Abstract

Magnesium is a promising light metal that has been widely used to manufacture components for automobiles, bicycles and electronics. By forecasting the demand for magnesium materials, we can recognize its prospects in these industries. Therefore, this study applies the GM(1,1) power model, the most frequently used gray prediction model, to forecast the demand for magnesium materials. Gray prediction is appropriate for this study, because there is little available data on magnesium material demand and it does not coincide with statistical assumptions. In contrast to the original GM(1,1) power model, which simply treats each sample with equal importance, this study uses gray relational analysis to estimate the weight of each sample to improve the prediction accuracy. The forecasting ability of the proposed gray residual modification models was verified using real data regarding magnesium material demand. The results showed that the proposed variant of the GM(1,1) power model offers performance that is comparable to other gray prediction models.

摘要

镁是一种很有前途的轻金属，已广泛用于制造汽车、自行车和电子产品的部件。通过预测镁材料的需求，我们可以看到它在这些行业的前景。因此，本研究应用最常用的灰色预测模型 GM(1,1) 幂模型来预测镁材料的需求。灰色预测适用于本研究，因为关于镁材料需求的可用数据很少，并且与统计假设不符。与原始GM(1,1)幂模型简单地将每个样本同等重要地对待不同，本研究使用灰色关联分析来估计每个样本的权重以提高预测精度。使用有关镁材料需求的真实数据验证了所提出的灰色残留改性模型的预测能力。结果表明，提出的 GM(1,1) 幂模型变体提供的性能可与其他灰色预测模型相媲美。