基于ARIMA和GM模型的青岛港货物吞吐量预测研究
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摘要
通过多元回归、时间序列模型以及灰色预测模型,对青岛市的近20 a对外贸易总额和吞吐量进行分析预测。充分考虑与港口货物吞吐量相关的六种因素指标,构建多元回归方程,运用Eviews软件对各因素的数据进行处理,建立ARIMA模型并对提取的三个指标进行预测,从而对回归模型中的对外贸易进出口总额(因变量)进行总预测,以了解青岛港的运输需求量;采用灰色预测模型并运用MATLAB软件对青岛港的货物吞吐量预测,分析青岛港港口的运输承载力。运用数学模型对港口吞吐量进行科学的评价和预测,能为青岛港制定中长期发展战略提供基本依据,对港口的持续发展的具有一定的现实意义。
Through multiple regression, time series model and grey prediction model, the total foreign trade volume and throughput of Qingdao in the past 20 years were analyzed and predicted.The six factors related to the port cargo throughput and construct multiple regression equations were fully considered.Eviews software was used to process the data of each factor, and an ARIMA model was established and the extracted three indicators were predicted. Thus, the total forecast of foreign trade import and export(dependent variable) in the regression model was predicted in order to understand the transportation demand in Qingdao Port.Based on GM, MATLAB software was used to predict the cargo throughput of Qingdao Port and the carrying capacity of Qingdao Port was analyzed.The scientific evaluation and prediction of port throughput by mathematical model can provide basic basis for Qingdao Port to formulate medium and long term development strategy and have certain practical significance for the sustainable development of the port.
Through multiple regression, time series model and grey prediction model, the total foreign trade volume and throughput of Qingdao in the past 20 years were analyzed and predicted.The six factors related to the port cargo throughput and construct multiple regression equations were fully considered.Eviews software was used to process the data of each factor, and an ARIMA model was established and the extracted three indicators were predicted. Thus, the total forecast of foreign trade import and export(dependent variable) in the regression model was predicted in order to understand the transportation demand in Qingdao Port.Based on GM, MATLAB software was used to predict the cargo throughput of Qingdao Port and the carrying capacity of Qingdao Port was analyzed.The scientific evaluation and prediction of port throughput by mathematical model can provide basic basis for Qingdao Port to formulate medium and long term development strategy and have certain practical significance for the sustainable development of the port.
引文
[1]王欣,刘春修.青岛港:打造“一带一路”双向桥头堡[J].走向世界,2017(33):26-29.WANG X,LIU C X.Qingdao Port:Building a “Band and Road” Bidirectional Bridgehead[J].Head to the World,2017(33):26-29.
[2]张美英,何杰.时间序列预测模型研究综述[J].数学的实践与认识,2011(18):189-195.ZHANG M J,HE J.A Review of Researches on Time Series Prediction Model[J].Mathematics Practice and Understanding,2011(18):189-195.
[3]李梦婉,沙秀艳.基于GM(1,1)灰色预测模型的改进与应用[J].计算机工程与应用,2016(4):24-30.LI M W,SHA X Y.Improvement and Application of Grey Prediction Model Based on GM(1,1)[J].Computer Engineering and Applications,2016(4):24-30.
[4]黄勇,高捷.最优组合预测方法在内河港口吞吐量预测中的应用[J].水道港口,2006(6):401-404.HUANG Y,GAO J.Application of the optimized combination model in throughput forecasting of inland river port[J].Journal of Waterway and Harbor,2006(6):401-404.
[5]王侃.环渤海港口群成长纪实(六)历史悠久的贸易良港——青岛港[J].港口经济,2015(6):53-55.WANG K.Record of the Growth of Bohai Rim Port Group (6) Long-established Trading Port-Qingdao Port[J].Port Economy,2015(6):53-55.
[6]秦芳,周鹏飞,魏晓晓,等.基于聚类的港口吞吐量预测方法及其适用性分析[J].水道港口,2011(3):223-228.QIN F,ZHOU P F,WEI X X,et al.Study on port throughput forecasting method and its application based on cluster analysis[J].Journal of Waterway and Harbor,2011(3):223-228.
[7]李雨蒙.基于ARIMA模型的辽宁港运输能力分析[D].南昌:南昌大学,2016.
[8]董博伦,徐东钰.基于ARIMA模型的农产品类股价预测与分析[J].现代商业,2015(3):186-188.DONG B L,XU D Y.Forecast and Analysis of Agricultural Products′ Stock Price Based on ARIMA Model[J].Modern Commerce,2015(3):186-188.
[9]张大维,刘博,刘琪.EViews数据统计与分析教程[M].北京:清华大学出版社,2010.
[10]樊文靖.基于最优组合的重庆港港口货物吞吐量预测模型研究[D].重庆:重庆交通大学,2014.
[2]张美英,何杰.时间序列预测模型研究综述[J].数学的实践与认识,2011(18):189-195.ZHANG M J,HE J.A Review of Researches on Time Series Prediction Model[J].Mathematics Practice and Understanding,2011(18):189-195.
[3]李梦婉,沙秀艳.基于GM(1,1)灰色预测模型的改进与应用[J].计算机工程与应用,2016(4):24-30.LI M W,SHA X Y.Improvement and Application of Grey Prediction Model Based on GM(1,1)[J].Computer Engineering and Applications,2016(4):24-30.
[4]黄勇,高捷.最优组合预测方法在内河港口吞吐量预测中的应用[J].水道港口,2006(6):401-404.HUANG Y,GAO J.Application of the optimized combination model in throughput forecasting of inland river port[J].Journal of Waterway and Harbor,2006(6):401-404.
[5]王侃.环渤海港口群成长纪实(六)历史悠久的贸易良港——青岛港[J].港口经济,2015(6):53-55.WANG K.Record of the Growth of Bohai Rim Port Group (6) Long-established Trading Port-Qingdao Port[J].Port Economy,2015(6):53-55.
[6]秦芳,周鹏飞,魏晓晓,等.基于聚类的港口吞吐量预测方法及其适用性分析[J].水道港口,2011(3):223-228.QIN F,ZHOU P F,WEI X X,et al.Study on port throughput forecasting method and its application based on cluster analysis[J].Journal of Waterway and Harbor,2011(3):223-228.
[7]李雨蒙.基于ARIMA模型的辽宁港运输能力分析[D].南昌:南昌大学,2016.
[8]董博伦,徐东钰.基于ARIMA模型的农产品类股价预测与分析[J].现代商业,2015(3):186-188.DONG B L,XU D Y.Forecast and Analysis of Agricultural Products′ Stock Price Based on ARIMA Model[J].Modern Commerce,2015(3):186-188.
[9]张大维,刘博,刘琪.EViews数据统计与分析教程[M].北京:清华大学出版社,2010.
[10]樊文靖.基于最优组合的重庆港港口货物吞吐量预测模型研究[D].重庆:重庆交通大学,2014.