摘要
随着经济飞速发展,我国快递业务量逐年上升,快递业的发展水平成为衡量区域经济和社会发展的重要指标。文章通过爬虫技术获取2016年1月—2022年11月江苏省的快递业数据,通过Anaconda平台使用季节性差分自回归滑动平均模型(Seasonal Autoregressive Integrated Moving Average,简称SARIMA)对获取的数据进行分析。考虑到原始数据为非平稳时间序列,进行差分处理和参数分析,最终确定模型为SARIMA(1,1,1)(0,1,2)12,结果表明该模型的数据拟合较好。通过模型对2022年12月—2023年5月的快递量进行预测。文章认为,预测模型的数据能更好地助力快递业解决可能发生的风险和不确定因素,为今后区域经济和区域快递业务发展提供重要参考。
With the rapid development of economy,the business volume of express industry in China has been increasing year by year,and the development of express industry has become an important indicator to measure regional economic and social development.This paper obtains the express data of Jiangsu Province from January 2016 to November 2022 through crawler technology,and uses Seasonal Autoregressive Integrated Moving Average(SARIMA)model on Anaconda platform to analyze the obtained data.Considering that the original data is non-stationary time series,difference processing and parameter analysis are carried out,and the model is finally determined as SARIMA(1,1,1)(0,1,2)12.The results show that the model fits the data well.Finally,the express volume data from December 2022 to May 2023 is predicted by the model.It is believed that the data of the prediction model can better assist the express industry to solve the possible risks and uncertainties,and provide reference for the future regional economic and regional express business development.
作者
安致远
何恩球
AN Zhiyuan;HE Enqiu(Chemical Equipment College,Shenyang University of Technology,Liaoyang 111003,China)
出处
《物流科技》
2022年第20期63-66,70,共5页
Logistics Sci-Tech
基金
2022年度辽宁省教育厅基本科研项目“石墨烯/丁腈橡胶复合材料的力学与摩擦学性能的多尺度研究”(LJKMZ20220515)。
