﻿ 电子商务增长模型的分析预测 Analysis and Prediction of Electronic Commerce Growth Model

Statistics and Application
Vol. 08  No. 01 ( 2019 ), Article ID: 28957 , 8 pages
10.12677/SA.2019.81020

Analysis and Prediction of Electronic Commerce Growth Model

Hongzhan Chen, Jing Zheng

The College of Economics, Hangzhou Dianzi University, Hangzhou Zhengjiang

Received: Feb. 2nd, 2019; accepted: Feb. 14th, 2019; published: Feb. 21st, 2019

ABSTRACT

Based on Chinese quarterly e-commerce transactions data in recent years, the quadratic, the exponential and the logistic growth models are fitted. Using MATLAB software, we plot the e-commerce growth data points as well as the forecast figure, and estimate the model parameters, goodness-of-fit statistics and model residual analysis. The analytic model of the fitting curve and forecast for the future scale of e-commerce data are given.

Keywords:Electronic Commerce, Logistic Growth Model, Residual Analysis

1. 引言

2. 模型介绍

2.1. 二次函数增长模型

$x\left(t\right)=a{t}^{2}+bt+c$

2.2. 指数函数增长模型

2.3. 阻滞增长模型

$\frac{\text{d}x}{\text{d}t}=r{\left(1-\frac{x}{{x}_{m}}\right)}^{x},x\left(0\right)={x}_{0}$

$x\left(t\right)=\frac{{x}_{m}}{1+\left(\frac{{x}_{m}}{x\left(0\right)}-1\right){\text{e}}^{-rt}}$

3. 实证检验

3.1. 模型的描述统计

Table 1. E-commerce transaction data in quarter from 2010 to 2016

Table 2. Statistical description of models

3.2. 三个模型的拟合结果描述

3.3. 三条曲线的拟合曲线及预测图

Figure 1. Raw data and fit curve

4. 模型的残差分析

4.1. 自相关分析检验残差的独立性

Figure 2. Residual autocorrelation coefficient

0的独立同分布随机变量序列。从图中我们可以得出残差项存在独立性。

4.2. 残差的正态分布检验

Figure 3. Histogram of residuals

4.3. 未来季度数据预测

Table 3. Quarterly Data Forecast in the future

5. 结束语

Analysis and Prediction of Electronic Commerce Growth Model[J]. 统计学与应用, 2019, 08(01): 176-183. https://doi.org/10.12677/SA.2019.81020

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