基于ARIMA乘积季节模型的港口集装箱吞吐量预测
Port Container Throughput Forecasting Based on the Multiplicative Seasonal ARIMA Model

作者: 陶丽丽 :大连海洋大学理学院,辽宁 大连 ; 王 艳 :大连海洋大学外国语学院,辽宁 大连 ;

关键词: 时间序列分析ARIMA乘积季节模型集装箱吞吐量预测Time Series Analysis Multiplicative Seasonal ARIMA Model Container Handling Capacity Forecast

摘要:
在对时间序列分析理论研究基础上,利用MATLAB软件编写所有算法的程序系统地分析港口集装箱吞吐量月度数据的变化规律,建立的ARIMA乘积季节模型能充分反映港口集装箱吞吐量的时间序列变化规律。以上海港2002~2009年集装箱吞吐量为例,应用MATLAB软件建立了ARIMA(0,1,1)×(0,1,1)12乘积季节模型,结果表明该乘积季节模型的预测精度较高,预测结果更加合理,有着广泛的应用前景。

Abstract: Based on the theoretical research of the time series analysis, this paper systematically analyzes the changes rules of the monthly data of container throughput of Shanghai Port from 2002 to 2009 by using MATLAB software. The result shows that the multiplicative seasonal ARIMA(0,1,1(0,1,1)12 model has a high forecasting precision, a reasonable forecasting result and a broad application prospect.

文章引用: 陶丽丽 , 王 艳 (2015) 基于ARIMA乘积季节模型的港口集装箱吞吐量预测。 运筹与模糊学, 5, 30-37. doi: 10.12677/ORF.2015.52005

参考文献

[1] 陈秀瑛, 古浩 (2010) 灰色线性回归模型在港口吞吐量预测中的应用. 水运工程, 5.

[2] 高尚, 梅亮 (2007) 基于支持向量机的港口吞吐量预测. 水运工程, 5.

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

[4] 安鸿志 (1992) 时间序列分析. 华东师范大学出版社, 上海.

[5] George, E.P.B., Gwilym, M.J. and Reinsel, C.G. (1994) Time series analysis: Forecasting & control. Prentice Hall.

[6] Hosking, J.M.R. (1984) Modeling presistence in hydrological time series using fractional differencing. Water Resources Research, 20, 1898-1908.

[7] Tiao, G.C. and Tsay, R.S. (1994) Some advances in non-linear and adaptive modeling in time series. Journal of Forecasting, 13, 109-131.

[8] Zhang, Y., Bi, P. and Hiller, J.E. (2010) Me-teorological variables and malaria in a Chinese temperate city: A twenty-year time-series data analysis. Environment International, 36, 439-445.

[9] Mohan, S. and Vedula, S. (1995) Multiplicative seasonal Arima model for longterm forecasting of inflows. Water Resources Management, 9, 115-126.

[10] 李勇 (2005) 基于乘积ARIMA模型的产品不确定性需求预测. 系统工程与电子技术, 1, 60-62.

[11] 梁鑫 (2006) 乘积季节模型在商品房市场中的应用研究. 广西师范学院学报, 2, 8-12.

[12] 乔治•博克斯, 格威利姆•詹金斯, 格雷戈里•莱因泽尔 (2011) 时间序列分析: 预测与控制. 机械工业出版社, 上海.

分享
Top