摘要
灰色-周期外延组合模型是在GM(1,1)基础上建立的残差周期外延模型,并提取优势周期以重新构造新的数据序列,再将不同周期同一时刻的值叠加。该模型克服了货运量的单调性和周期波动性给预测带来的困难。通过利用该模型的优点,对某公司的货运量动态变化进行预测,说明该模型可明显提高货运量的预测精度。
The Gray-periodic extensional combinatorial model belongs to an error periodic extensional model based on GM（1,1） with preferred period to contrast new data sequences, then to add the values at the same moment of different periods. The model overcomes the unpredictability due to monotonicity and periodical fluctuation of freight volume. The model is applied for prediction of a company＇ s freight volume and the result shows that the model could significantly improve the forecast accuracy.
出处
《铁道运输与经济》
北大核心
2009年第2期62-65,共4页
Railway Transport and Economy
关键词
货运量
GM(1
1)
灰预测
周期外延组合模型
Freight Volume
GM（1,1）
Grey Forecast
Periodic Extensional Combinatorial Model
