Author(s)

Hantao Zhang ¹, Xiaoxuan Xie ¹

Affiliation(s)

¹ School of Economics and Modern Finance, Gannan University of Science and Technology, Ganzhou, China

Corresponding Author

Hantao Zhang

ABSTRACT

Today in the Internet era, online shopping has become an indispensable part of life, then the transportation of e-commerce logistics has become a major problem, if the logistics site is out of service, it will inevitably lead to problems in processing and transportation, at this time, it is necessary to predict the processing and transportation capacity of each logistics site, to ensure that the logistics of the normal operation of the logistics, and at the same time, designing alternatives can greatly reduce the impact of the out-of-service. This paper establishes a prediction model combining ARMA and LSTM to carry out an in-depth study on the emergency call of logistics and logistics network. In this paper, we first pre-processed the data, made a pivot table based on the existing data, which is convenient for observation and application, and then established an ARMA model, and found that the prediction results were inaccurate, and then combined with the LSTM neural network to weight the value of the prediction, and finally obtained the DC14→DC10, DC20→DC35, DC25→DC62 three lines from January 1, 2023 to January 31, 2023 daily cargo volume.

KEYWORDS

E-Commerce Logistics, ARMA, LSTM, Neural Network, Logistics Network

CITE THIS PAPER

Hantao Zhang, Xiaoxuan Xie, E-commerce Logistics Transportation Prediction Problem Based on ARMA and LSTM Neural Networks. Industrial Engineering and Innovation Management (2024) Vol. 7: 74-80. DOI: http://dx.doi.org/10.23977/ieim.2024.070110.

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