基于GIOWA算子的铁路客运量组合预测研究
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摘要
对全国铁路客运量进行高效精确地预测,对铁路部门发展有极其重要的战略意义。选取1997-2017年全国铁路客运量数据,采用时间序列ARIMA模型、Hlot-Winters非季节指数平滑和多元线性回归3种单项预测模型方法,将广义诱导因子引入,选取了IOWHA、IOWGA、IOWA算子,以最小误差平方和为限制建立组合预测模型并验证模型有效性。结果显示,组合预测模型精度与效用完全高于单项预测模型。结合我国铁路运输业的发展,在现有模型的支持下预测了未来5年的全国铁路客运量,以期为交通规划的制定与管理提供参考。
Precise and effective prediction of the railway passenger volume is of great strategic importance for the development of railway departments. Based on the data of railway passenger volume from 1997 to 2017 in China,this paper,first uses three individual forecasting models,time series-ARIMA model fitting,Hlot-winters non-seasonal index smoothing,multiple linear regression,to predict,and then,introducing the generalized induced ordered weighted averaging( GIOWA) operator and selecting IOWA,IOWHA,IOWGA operators,establishes the minimum error variance combination forecast models,and examines their validity. The results show that the combination forecast models have higher precision and effectiveness than individual forecasting models. In the end,considering the development of China's railway transportation industry,we predict the railway passenger volume during the next five years by using the existing models with the hope of providing references for the transportation planning and management.
Precise and effective prediction of the railway passenger volume is of great strategic importance for the development of railway departments. Based on the data of railway passenger volume from 1997 to 2017 in China,this paper,first uses three individual forecasting models,time series-ARIMA model fitting,Hlot-winters non-seasonal index smoothing,multiple linear regression,to predict,and then,introducing the generalized induced ordered weighted averaging( GIOWA) operator and selecting IOWA,IOWHA,IOWGA operators,establishes the minimum error variance combination forecast models,and examines their validity. The results show that the combination forecast models have higher precision and effectiveness than individual forecasting models. In the end,considering the development of China's railway transportation industry,we predict the railway passenger volume during the next five years by using the existing models with the hope of providing references for the transportation planning and management.
引文
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[3]KURHAN M B,KURHAN D M.Forecasting of passenger traffic upon implementation of high-speed running[J].2017,1(67):117-130.
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[5]刘婧,赵军.铁路旅客运量的组合预测模型[J].四川兵工学报,2008,29(5):111-113.
[6]侯丽敏,马国峰.基于灰色线性回归组合模型铁路客运量预测[J].计算机仿真,2011,28(7):1-3,30.
[7]刘琳玥.基于PCA-BP神经网络的铁路客运量预测模型研究[J].综合运输,2016(8):43-47,73.
[8]韦晨珺娃,杨桂元,袁宏俊.基于GIOWA算子的安徽省全社会用电量组合预测研究[J].嘉兴学院学报,2017(1):57-63.
[9]张晓峒.计量经济学软件Eviews使用指南[M].天津:南开大学出版社,2004:35-47.
[10]钟韬.基于IOWA算子的组合预测在四川省民用车数量中的应用研究[D].成都.西南交通大学,2010.
[11]孙浩,杨桂元.基于一种贴近度的IOWHA算子预测模型的性质研究[J].延边大学学报(自然科学版),2017,43(2):113-118.
[12]金晓武,庞新宇,杨兆建,等.基于IOWGA算子的转子系统磨粒浓度组合预测方法[J].润滑与密封,2017,42(3):82-85.
[13]方向华,王文杰,汤兵勇.基于模糊补偿的组合预测方法研究[J].中国储运,2006(1):77-79.
[14]陈华友,刘春林.基于IOWA算子的组合预测方法[J].预测,2003,22(6):61-65.
[15]张梦龙.基于公共物品属性视角的铁路改革结构特性研究[D].北京:北京交通大学,2014.
[2]JONAS B A,MINDAUGAS M A,Vladas LK,et al.Analysis and forecast of the dynamics of passenger transportation by public land transport[J].Transport,2004,19(1):3-8.
[3]KURHAN M B,KURHAN D M.Forecasting of passenger traffic upon implementation of high-speed running[J].2017,1(67):117-130.
[4]陈华友,盛昭瀚.一类基于IOWGA算子的组合预测新方法[J].管理工程学报,2005,19(4):36-39.
[5]刘婧,赵军.铁路旅客运量的组合预测模型[J].四川兵工学报,2008,29(5):111-113.
[6]侯丽敏,马国峰.基于灰色线性回归组合模型铁路客运量预测[J].计算机仿真,2011,28(7):1-3,30.
[7]刘琳玥.基于PCA-BP神经网络的铁路客运量预测模型研究[J].综合运输,2016(8):43-47,73.
[8]韦晨珺娃,杨桂元,袁宏俊.基于GIOWA算子的安徽省全社会用电量组合预测研究[J].嘉兴学院学报,2017(1):57-63.
[9]张晓峒.计量经济学软件Eviews使用指南[M].天津:南开大学出版社,2004:35-47.
[10]钟韬.基于IOWA算子的组合预测在四川省民用车数量中的应用研究[D].成都.西南交通大学,2010.
[11]孙浩,杨桂元.基于一种贴近度的IOWHA算子预测模型的性质研究[J].延边大学学报(自然科学版),2017,43(2):113-118.
[12]金晓武,庞新宇,杨兆建,等.基于IOWGA算子的转子系统磨粒浓度组合预测方法[J].润滑与密封,2017,42(3):82-85.
[13]方向华,王文杰,汤兵勇.基于模糊补偿的组合预测方法研究[J].中国储运,2006(1):77-79.
[14]陈华友,刘春林.基于IOWA算子的组合预测方法[J].预测,2003,22(6):61-65.
[15]张梦龙.基于公共物品属性视角的铁路改革结构特性研究[D].北京:北京交通大学,2014.