基于季节性时间序列的物流企业货运需求预测研究与应用
Research and Application of Logistics Enterprise Freight Demand Forecasting Based on Seasonal Time Series

作者: 罗维 , 方晓平 :中南大学交通运输工程学院,湖南 长沙 ;

关键词: 趋势外推法季节分解法Logistic曲线货运量预测Trend Extrapolation Method Seasonal Decomposition Method Logistic Model Freight Volume Forecasting

摘要: 货运量预测是制定合理物流计划的重要内容。本文以A物流公司的历史货运量为例,主要基于趋势外推法和季节分解法来进行研究,建立趋势预测单项模型和季节分解-趋势外推的组合模型。探讨对时间序列的短期趋势预测是直接曲线拟合还是“剔除”季节性后再拟合,对于趋势模型的选择,对比多项式拟合和Logistic曲线拟合的效果,同时假设季节因素对每个月的影响是不变的,最终构建四个预测模型,通过比较四种模型的拟合优度来评判出合适的预测模型。预测结果证明组合模型的拟合效果要比单项模型的要好。

Abstract: Freight volume forecasting is an important part of the reasonable logistics planning. In this paper, a historical freight volume of A company is regarded as research data, mainly basing on the trend extrapolation method and the seasonal decomposition method, establishing a single model and a combination model. Discussion on the time series of short-term forecasting is a direct curve or “remove” seasonal before fitting. For the trend model, there are two choices: One is polynomial model; another is Logistic model. While assuming the influence of monthly seasonal factors is same. We could build four predictive models ultimately, compare the goodness of fitting, then chose the best one. The result demonstrates that the combination model is better than the single model.

文章引用: 罗维 , 方晓平 (2016) 基于季节性时间序列的物流企业货运需求预测研究与应用。 管理科学与工程, 5, 7-14. doi: 10.12677/MSE.2016.51002

参考文献

[1] Russo, F. and Conigliaro, G. (1997) Integrated Macroeconomic and Transport Models for Freight Demand. Transpor-tation Systems, 3, 16-18.

[2] 王红, 宋风杰. 预测技术在港口吞吐量预测中的应用[J]. 实用物流技术, 1995(3): 8-11.

[3] Bashir, Z. and E1-Hawary, M.E. (2000) Short-term Load Forecasting by Using Wavelet Neural Networks. Canadian Conference on Electrical and Computer Engineering, 1, 163-166.

[4] Kavussanos, M.G. and Nomikos, N.K. (2000) Constant vs. Time-Varying Hedge Ratios and Hedging Efficiency in the BIFFEX Market. Transportation Research Part E: Logistics and Transportation Review, 36, 229-248.
http://dx.doi.org/10.1016/S1366-5545(99)00029-0

[5] 白世贞, 刘莉, 杨艳玲. 基于灰色模型与季节指数的物流需求预测研究——以哈尔滨市为例[J]. 物流工程与管理, 2010(6): 8-10.

[6] 关宏志, 陈艳艳. 地区间货物运输量预测方法谱系[J]. 土木工程学报, 2003(7): 47-52.

[7] 陈治亚, 周艾飞, 谭钦之, 方晓平. 基于改进的BP人工神经网络的物流需求规模预测. 铁道科学与工程学报, 2008(6): 62-68.

[8] 陈实. 货运量预测方法及应用研究[D]. 武汉: 武汉理工大学, 2008.

[9] 包训艳. 物流企业需求预测[J]. 中国电子商务, 2013(23): 258-259.

[10] 王平. 青岛港物流公司物流量预测分析[J]. 青岛远洋船员学院学报, 2008, 29(2): 53-57.

[11] Barrow, M. (2009) Statistics for Economics, Accounting and Business Studies. 5th Edition, Pearson Education, London.

[12] 高孝伟, 许涛, 郑林昌, 刘静. 对季节指数计算方法的思考[J]. 统计与决策, 2006(9): 155.

[13] Fang, X., Ansell, J. and Chen, W. (2013) Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession. Discrete Dynamics in Nature & Society, 2013, 1-10.

[14] 石宏. 第三方物流企业的运输需求分析模型研究与应用[D]. 长沙: 中南大学, 2013.

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