基于ARIMA模型的新疆快递行业发展研究

Research on the Development of Xinjiang’s Express Delivery Industry Based on the ARIMA Model

摘要: 在新疆经济加速发展，电商产业日益繁荣的背景下，快递行业规模呈现持续扩张态势。精准预测快递业务量对于合理配置行业资源，优化发展策略具有重要意义。本文基于2002~2023年《中国统计年鉴》中新疆快递量数据，采用ARIMA模型开展相关研究。首先，对原始时间序列数据进行平稳性检验，若不平稳则进行差分处理使其平稳。随后，通过分析自相关函数(ACF)和偏自相关函数(PACF)，确定ARIMA模型的参数p = 1，d = 2，q = 3。接着构建ARIMA(1,2,2)模型，对模型参数进行估计，并严格检验模型的显著性和拟合优度。在模型通过检验后，运用该模型对2024~2027年新疆快递量进行预测。研究结果表明，ARIMA(1,2,2)模型能够有效捕捉新疆快递量时间序列的内在规律，为新疆快递行业在未来业务规划，运力调配，设施建设等方面提供科学量化依据，助力新疆快递行业的可持续，高质量发展。

Abstract: Against the backdrop of Xinjiang’s accelerating economic development and the growing prosperity of its e-commerce industry, the scale of the express delivery industry has shown a sustained expansion trend. Accurately, predicting express delivery business volume is of great significance for the rational allocation of industry resources and the optimization of development strategies. This study uses the ARIMA model to conduct relevant research based on the express delivery volume data of Xinjiang from the China Statistical Yearbook (2002~2023). First, a stationarity test is performed on the original time series data. If the data is non-stationary, differencing processing is carried out to make it stationary. Subsequently, the parameters p = 1, d = 2, and q = 3 of the ARIMA model are determined by analyzing the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). Then, an ARIMA(1,2,2) model is constructed, model parameters are estimated, and the significance and goodness-of-fit of the model are strictly tested. After the model passes the test, it is used to predict Xinjiang’s express delivery volume from 2024 to 2027. The research results show that the ARIMA(1,2,2) model can effectively capture the internal laws of the time series of Xinjiang’s express delivery volume, providing a scientific and quantitative basis for future business planning, transportation capacity allocation, facility construction, and other aspects of Xinjiang’s express delivery industry, and facilitating the sustainable and high-quality development of the industry.