%0 Journal Article
%T 我国快递业务收入的时间序列分析——基于ARIMA模型<br>Time Series Analysis of China’s Express Business Revenue—Based on ARIMA Model
%A 韩薇薇
%J E-Commerce Letters
%P 466-474
%@ 2168-5851
%D 2024
%I Hans Publishing
%R 10.12677/ECL.2024.131056
%X 本研究选取2008年1月~2023年11月全国快递业务收入的月度数据为样本，采用时间序列检验方法对其进行了相关分析，然后根据数据特征建立了ARIMA乘法模型来拟合此时间序列。接着，我们用拟合模型来预测2022年3月和4月的全国快递业务收入，通过分析2022年3月和2022年4月全国快递业务收入预测值和真实值的差值，得出突发事件对我国快递行业有着短期负向冲击作用的结论。其次，我们用2008年~2019年的数据对2020年的全国快递业务收入做了预测并且分析预测值和实际值之间的差距以判断2020年初突发事件对全国快递业务收入的影响。最后，我们用2008年~2022年的数据对2023年的数据做出预测，对比实际值和预测值的差距我们发现：快递收入很快恢复至突发事件发生之前水平并有所增长。这对于电商快递行业在遭遇不可抗力因素导致的业务影响时，如何在抵抗短期负面影响的同时也兼顾到未来长期发展具有一定的借鉴意义。<br />
In this study, we selected the monthly data of national express delivery business revenue from January 2008 to November 2023 as a sample, used the time series test method to analyse its correlation, and then built an ARIMA multiplication model based on the data characteristics to fit this time series. Then, we use the fitted model to predict the national express business revenue in March and April 2022, by analysing the difference between the predicted value and the real value of the national express business revenue in March 2022 and April 2022, we conclude that emergencies have a short-term negative impact on China’s express industry. Secondly, we use the data from 2008~2019 to forecast the national express business revenue in 2020 and analyse the difference between the forecast value and the actual value to judge the impact of the unexpected events on the national express business revenue in early 2020. Finally, we use the data from 2008~2022 to make a forecast for 2023, and compare the difference between the actual value and the forecast value, we find that the express delivery revenue quickly returns to the previous level and grows. This is of some significance to the e-commerce express industry in the event of force majeure factors caused by the business impact, how to resist the short-term negative impact while also taking into account the future long-term development.
%K 快递业务收入，时间序列分析，ARIMA模型<br>Express Business Revenue
%K Time Series Analysis
%K ARIMA Model
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=82138