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DU Gang, LIU Ya-Nan. Forecasting of Shanghai Port container throughput under seasonal variation influence[J]. Journal of East China Normal University (Natural Sciences), 2015, (1): 234-239. doi: 10.3969/j.issn.1000-5641.2015.01.028
Citation: DU Gang, LIU Ya-Nan. Forecasting of Shanghai Port container throughput under seasonal variation influence[J]. Journal of East China Normal University (Natural Sciences), 2015, (1): 234-239. doi: 10.3969/j.issn.1000-5641.2015.01.028

Forecasting of Shanghai Port container throughput under seasonal variation influence

doi: 10.3969/j.issn.1000-5641.2015.01.028
  • Received Date: 2014-11-01
  • Publish Date: 2015-01-25
  • 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.
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  • [1]
    包起帆,江霞.上海港发展面临的问题和未来空间拓展研究[J].水运工程, 2013(3):1620.

    PENG W Y, CHU C W. A comparison of univariate methods forforecasting container throughput volumes[J]. Mathematical and Computer Modeling, 2009, 50(78):10451057.

    CHE S H, CHEN J N. Forecasting container throughputs at ports using genetic programming [J]. Expert Systems with Applications, 2010, 37(3): 20542058.

    XIE G, WANG S Y, ZHAO Y X, et al. Hybrid approaches based on LSSVR model for container throughputforecasting: A comparative study [J]. Applied Soft Computing, 2013, 13(5): 22322241.

    刘婷,林连.港口集装箱吞吐量预测方法研究[J].苏州科技学院学报,2011,24(4):4446.

    程荣,吴国付,张玉洁.改进的RBF神经网络在港口集装箱吞吐量预测中的应用[J].水运工程, 2004(8):1320.

    柳艳娇,肖青.组合模型在港口集装箱吞吐量预测中的应用[J].大连海事大学学报, 2006,32(3): 2528.

    杨中庆,赵彬彬,廖慧敏.灰色组合模型在港口集装箱吞吐量预测中的应用[J].水运工程, 2006(9): 1425.

    戴燚,王锡淮,肖健梅.支持向量机在集装箱吞吐量上的预测[J].水运工程, 2005(8):1839.

    张浩.基于最优线性组合的港口集装箱吞吐量预测方法[J]. 武汉理工大学学报:交通科学与工程版,2007, 31(2): 373376.

    赵亚鹏,丁以中.基于GRNN神经网络的长江干线港口集装箱吞吐量预测[J]. 中国航海, 2006(4):90100.

    宁顺理,张亦飞,毛翰宣.宁波—舟山港集装箱吞吐量的SD建模预测[J].水运工程, 2011(4):5876.

    张健.SARIMA模型在预测中的CPI中的应用[J].统计与决策, 2011(5):2830.
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