华东师范大学学报(自然科学版) ›› 2015, Vol. 2015 ›› Issue (1): 234-239.doi: 10.3969/j.issn.10005641.2015.01.028

• 地理学 河口海岸学 • 上一篇    下一篇

季节性变动影响下的上海港集装箱吞吐量预测

杜刚,刘娅楠   

  1. 华东师范大学 商学院,上海 200241
  • 收稿日期:2014-11-01 出版日期:2015-01-25 发布日期:2015-03-29
  • 通讯作者: 杜刚,男,博士,研究方向为集装箱运输与优化 E-mail:gdu@dbm.ecnu.edu.cn
  • 基金资助:

    国家自然科学基金面上项目( 71472065);上海市软科学研究计划重点项目(14692105700);教育部人文社会科学基金青年项目(14YJC630026);上海市浦江人才计划项目(14PJC027)

Forecasting of Shanghai Port container throughput  under seasonal variation influence

 DU  Gang, LIU  Ya-Nan   

  1. Business School, East China Normal University, Shanghai 200241, China
  • Received:2014-11-01 Online:2015-01-25 Published:2015-03-29

摘要: 港口吞吐量精准预测对于每一个港口的成功经营和有效决策都十分重要.季节性波动经常会影响港口吞吐量,为了更为准确地预测上海港口集装箱吞吐量,本文选取2007年至2012年上海港母港集装箱吞吐量的月度数据,并对于港口集装箱吞吐量的月度数据中出现的季节性波动进行了处理,采用季节时间序列模型对其进行预测.为了说明方法的有效性,以同样的数据,使用整自回归移动平均模型对上海港集装箱吞吐量进行预测.两种方法预测结果进行对比发现,利用季节时间序列模型对港口集装箱吞吐量季节性进行处理,能够提高港口集装箱吞吐量的预测精度.

关键词: 单整自回归移动平均模型, 季节时间序列模型, 港口集装箱吞吐量, 预测

Abstract: It is very important for successful port operation and effective decisionmakingby forecasting container throughput accurately. The Autoregressive Integrated Moving Average model (ARIMA) and the Seasonal Autoregressive Integrated Moving Average model(SARIMA) are applied to the monthly data from 2007 to 2012 of Shanghai Port container throughput to forecast the container throughput of Shanghai Port. The seasonal variation of monthly port container throughputdata can be handled by SARIMA. Compared with ARIMA, SARIMA performed better and improved the Port container throughputprediction accuracy because of removing the seasonal variation.

Key words: Autoregressive Integrated Moving Average model (ARIMA), Seasonal Autoregressive Integrated Moving Average model (SARIMA), port container, throughput;, forecasting

中图分类号: