摘要
不同预测模型在同一时刻都存在不同的预测误差,最优组合预测的预测精度要比其他模型高,但并不是所有时刻误差都最小,有的时刻可能比单项预测误差还大,因此,在预测时就存在较大的风险.再组合预测可以降低预测风险,但方法比较复杂难以推广.针对这些问题,根据生物遗传原理,设计了一种优势重组系统,子代的基因由父代的最优基因重组得到,有着优良基因的子代预测的结果误差最小或不是最大.实证结果表明,优势重组模型预测性能高于所有父代模型.
The different models of the forecasts have the different errors at the same time. The optimum combination forecasts model produce more accuracy than others, but its errors are not the lowest one at every time in the forecast period for the reasons that the accuracy is the mean value. There would be forecast risk at some time. The recombination forecasts may reduce the risk of the forecast. The complexity of the method, however, blocks the popularization. For the reasons above, we design the system of the hybrid model for forecasts which can use the best gene from the parents at the different time according to the variation of data and it makes the errors the lowest or not the highest. The empirical result reveals that the system can select the out-performance gene or the non-inferior one.
出处
《山西师范大学学报（自然科学版）》
2008年第2期9-14,共6页
Journal of Shanxi Normal University(Natural Science Edition)
关键词
组合预测
优势基因重组
预测精度
广义回归神经网络
aombination forecasts
superior gene recombination
accuracy of forecasts
general regression neural network
