摘要
在传统的灰色预测模型中,传统背景值公式和初始值的选取方法会造成模型产生偏差。通过对背景值公式的改进和初始值选取方法的变化,构造出预测精度更高的灰色模型,即优化GM(1,1),并将优化GM(1,1)与马尔可夫链预测相结合,对我国的铁路货运量进行了预测分析,得出了相应的预测区间及其发生概率。通过理论分析和算例表明,该方法的预测结果比传统灰色模型预测结果更加可靠。
In the traditional gray prediction model, the traditional background value formula and initial value selection method could result in deviation of the model. Though improving the background value formula and changing the initial value selection method, the paper constructs a higher predition accuracy gray model-optimized GM （1,1） and integrates it with Markov chain. The paper analyzes and predicts railway freight volume in China and concludes on the prediction interval and the probability of occurrence. A subsequent examples shows that the method is more reliable than its traditional counterpart.
出处
《物流技术》
2011年第7期129-131,142,共4页
Logistics Technology
基金
铁道部科技开发项目<铁路物流发展相关技术研究-铁路物流管理信息相关技术研究>(2010X003-G)
