灰色成分数据模型在中国产业结构分析预测中的应用

针对成分数据这种特殊类型的统计数据,提出一种新的预测建模方法:对于一列按照时间顺序收集的成分数据,先运用对数变换使成分数据降维,然后对降维后的数据运用GM(1,1)模型进行预测,最后再将预测值进行反对数变换,从而得到了各成分的预测值.根据提出的方法,建立了中国产业结构的预测模型,并分析了中国产业结构的发展趋势和未来状况.经检验,运用该方法预测出的数据与实际值十分吻合.

The Grey Compositional Data Model and Its Application in the Analysis and Forecast of China's Industrial Structure

Considering that compositional data is a special kind of statistical data,we propose a new(forecasting) and modeling method: for a set of successive compositional data collected according to the time order,firstly the logarithmic transform is used to reduce dimensions of compositional data,secondly GM(1,1) model is used to forecast the data whose dimensions have been reduced,finally a logarithmic inverse transform is used to the forecasting value and the forecasting value of each composition is got accordingly.Based on the method this paper proposes,we found a forecasting model of Chinese industrial structure,and analyze the developing trend and future status of Chinese industrial structure.Having been tested,the fitted data by the method we propose agree with the real values quite well.