基于改进加权聚类的煤炭价格区间型组合预测模型

Interval Combination Forecasting Model of Coal Price Based on Improved Weighted Clustering

准确预测煤炭价格可以提高煤炭销售决策的科学性,为了提高煤炭价格预测精度,提出基于改进加权聚类的煤炭价格区间型组合预测模型.从制造费用、煤炭产量、煤炭消费和库存变化等方面分析了影响煤炭价格的因素.根据煤炭价格数据的波动性特点定义了小波变换函数,通过消除煤炭价格数据噪声完成煤炭价格数据的预处理.在引入多属性决策中的区间数相离度概念基础上,利用改进加权聚类法确定煤炭价格区间组合预测权重,通过计算煤炭价格区间型组合预测的加权系数,搭建煤炭价格区间型组合预测模型,实现煤炭价格的预测.仿真结果表明,文中方法在预测煤炭价格时,可以将均方根误差和平均绝对误差分别控制在0.1~0.3之间和0.2以内,大大提高了煤炭价格预测精度.

Accurate prediction of coal price can improve the scientific sales decision on coal. An interval combination prediction model of coal price based on improved weighted clustering is proposed to improve the accuracy of coal price prediction. This paper analyzes the factors affecting coal production and production from the aspects of coal price and inventory. According to the volatility characteristics of coal price data, the wavelet transform function is defined, and the preprocessing of coal price data is completed by eliminating the noise of coal price data. On the basis of introducing the concept of interval number separation degree in multi-attribute decision-making, the improved weighted clustering method is used to determine the prediction weight of coal price interval combination. By calculating the weighting coefficient of coal price interval combination prediction, the coal price interval combination prediction model is established to realize the prediction of coal price. The simulation results show that the root mean square error and average absolute error can be controlled between 0.1 ~ 0.3 and 0.2 respectively, which greatly improves the prediction accuracy of coal price.