改进的灰色预测模型在煤矿成本预测中的应用

The application of improved grey model on coal cost forecasting

GM(1,1)模型是进行成本预测的普遍方法,但传统的GM(1,1)模型却难以反映序列的随机波动性。本文通过对传统的GM(1,1)模型进行改进,将原始时间序列进行加速平移变换和几何平均变换,有效地弱化了原始数据的随机性,将改进的GM(1,1)模型应用于某矿的原煤成本预测,对该矿区2003～2011年的单位原煤成本进行相对误差检验,预测结果表明该改进模型预测效果较好,为煤矿企业进行成本预测提供了一种适用的工具。

GM（1,1） is a common method of cost forecasting, but this traditional grey model cannot reflect the random volatility of the time sequence. The improved GM （1, 1） can be obtained through accelerating translation transform and geometric mean transform. This improved model can decrease the random of the original sequence. In this paper this improved GM（1,1） is used to predict the coal cost of a coal mine. The error test shows that this method can he used in the coal cost prediction and provides a satisfactory tool for coal mine cost prediction.