﻿ 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

Management Science and Engineering
Vol.05 No.01(2016), Article ID:17098,8 pages
10.12677/MSE.2016.51002

Research and Application of Logistics Enterprise Freight Demand Forecasting Based on Seasonal Time Series

Wei Luo, Xiaoping Fang

School of Traffic and Transportation Engineering, Central South University, Changsha Hunan

Received: Feb. 19th, 2016; accepted: Mar. 5th, 2016; published: Mar. 9th, 2016

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.

Keywords:Trend Extrapolation Method, Seasonal Decomposition Method, Logistic Model, Freight Volume Forecasting

1. 引言

2. 理论基础

(1)

(2)

2.1. 趋势外推模型

(1) 多项式模型：

(3)

(2) Logistic模型：

，其中K为饱和值(4)

2.2. 季节分解法

(5)

(6)

(7)

(8)

(9)

(10)

3. 模型应用

3.1. 数据描述

A物流公司主要经营国内的公路零担货物运输业务。目前运输服务的线路基本上覆盖了全国各个省、市、自治区，服务网络遍及全国，已开设直营网点5000多家，自有的营运车辆近9000台，分布在全国的转运中心的总面积超过100万平方米。A公司在2011年的货物吞吐量达到近500万吨。本文数据是A公司自2009年1月至2011年12月共36个月的货运量记录。时间序列记为：

3.2. 模型建立

3.2.1. 趋势模型建立

(1) 直接趋势外推建模

A) 用多项式模型分别进行拟合，拟合效果如图2所示。对拟合模型进行参数检验，其中二次、三次模型的拟合度相等，而且拟合优度要高于线性模型。如表1所示。

Figure 1. Sequence chart

B) Logistic曲线拟合要输入上限值，故令K取不同值。输出结果如表2所示。

(2) 季节分解模型与趋势外推模型组合建模

A) 对SER01_TC进行多项式趋势拟合，结果如表3

Figure 2. Polynomial model fitting

Table 1. Polynomial model aggregation and parameter test

Table 2. Model goodness of different K

Figure 3. Sequence after seasonal decomposition of freight volume

Table 3. Polynomial model aggregation and parameter test of SER01_TC

B) 对SER01_TC序列进行Logistic模型拟合，令K取如下值，输出不同K值时的R2。见表4

3.2.2. 季节因子

Table 4. Model goodness of different K of SER01_TC

Table 5. Seasonal factors of each model

Table 6. R2 of forecasting model

3.3 模型拟合优度

(11)

4. 结论

Research and Application of Logistics Enterprise Freight Demand Forecasting Based on Seasonal Time Series[J]. 管理科学与工程, 2016, 05(01): 7-14. http://dx.doi.org/10.12677/MSE.2016.51002

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