标题:
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
关键字:
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
期刊名称:
《Management Science and Engineering》, Vol.5 No.1, 2016-03-09
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。
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.