摘要
二重趋势时间序列的预测结果与其整体趋势变动性和季节波动性的特征有较大的关系,在采用神经网络并针对其特征的基础上还没有较好的预测方法.本文以圆形反向传播网络(CBP)为基础,提出二重趋势时间序列预测组合模型(CCBP),即根据二重趋势时间序列特征对其趋势分量与周期分量采用圆形反向传播网络分开进行预测,再对二者积乘.以云南省某片区若干卷烟销售网点的采样数据为研究实例,证实了CCBP模型在二重趋势时间序列预测方面性能有所提高.
The prediction performance of double trend time series has dependence relation to its character： the trend change nature and season fluctuation characteristic. However, there is no effective way to construct the forecast method with the Neural Network and this double trend character. Based on circular back propagation network（CBP）, a combined forecast model-CCBP is proposed which separates the weight of trend and the weight of fluctuation from predicted series data, respectively predicts the two weights, then multiplies two weight. After comparatively studying the data cases of some Yunnan tobacco company, it is proved that the CCBP model in the double trend time series has effectively enhanced the prediction performance.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2007年第6期1015-1020,共6页
Control Theory & Applications
