Academic Journal of Computing & Information Science, 2023, 6(8); doi: 10.25236/AJCIS.2023.060812.
Yuqing Weng, Zirui Wang
School of International Business, Tianjin Foreign Studies University, Tianjin, 300270, China
In a commercial logistics network, logistics sites and logistics routes are the key links that make up the logistics transportation process. Therefore, accurate prediction of cargo volume of each logistics site and route is essential to improve logistics operation efficiency, reduce costs, and ensure smooth logistics transportation. In order to predict the cargo volume of logistics routes, the historical cargo volume data of the three routes to be predicted are firstly compiled, and the data are analyzed by using ARIMA time series due to the time-series nature of the data. Since the data are smoothed in the second-order difference, the optimal parameter values are calculated after the second-order difference, and the ARIMA(1,2,3) time series prediction model is established to predict the cargo volume data of the three routes from 2023-1-1 to 2023-1-31.
E-commerce logistics; Cargo volume; ARIMA time series
Yuqing Weng, Zirui Wang. ARIMA time series based logistics route cargo volume forecasting research. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 8: 95-104. https://doi.org/10.25236/AJCIS.2023.060812.
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