基于X11-ARIMA模型的铁路货运周转量分析  

Analysis the Turnover of Railway Freight Transport Based on X11-ARIMA Model

作　　者：张立欣[1] 张艳波[1] 杨翠芳 ZHANG Li-xin, ZHANG Yan-bo, YANG Cui-fang(College of Information Engineering, Tarim University, Alar 843300, Chin)

机构地区：[1]塔里木大学信息工程学院

出　　处：《数学的实践与认识》2018年第17期154-161,共8页Mathematics in Practice and Theory

基　　金：国家自然科学基金项目(61662064)

摘　　要：铁路货运周转量分析对铁路规划建设以及运营决策有着重要的意义.根据近12年的铁路货运周转量月度数据,利用X11-ARIMA模型进行分析,结果表明：铁路货运周转量的发展基本上经历了线性快速增长、平稳后下降和再增长这样的长期趋势.从季节效应来看,2012年之前的季节效应比较一致,之后季节效应减弱.综合考虑长期趋势和季节效应的影响,用ARIMA（1,1,1）×（1,1,0）_（12）拟合该序列的发展,以2017年1月和6月的数据为考核样本,检验结果表明该模型对数据的预测效果较好.Analysis the turnover of railway freight transport is of great significance to railway planning, construction and operation decision. According to the monthly data in recent 12 years, the Xll-ARIMA model was adopted to analyze the changing rule. The results show that the development of the turnover of railway freight transport has undergone a trend of rapid growth, steady decline and re-growth. The seasonal effect before 2012 is more consistent than later. Considering the influence of long-term trend and seasonal effect, ARIMA（1, 1, 1）× （1, 1,0）12 model was proposed to fit the development of the sequence. With these data that from January to June in 2017 as the comparison sample, the model predicted the data very precisely.

关 键 词：长期趋势 季节效应 序列 

分 类 号：U294.13[交通运输工程—交通运输规划与管理]