Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024)

<Previous Article In Volume
Next Article In Volume>

Regional Express Business Volume Forecasting Based on Combinatorial Modelling

Authors
Xiaoshan Yan1, *, Yanbin Wang2, Jingwei Zhang1
1Class of 2022 Master’s Degree Students, School of Business Administration, Liaoning Technical University, Huludao, Liaoning, 125105, China
2Associate Professor, School of Business Administration, Liaoning Technical University, Huludao, Liaoning, 125105, China
*Corresponding author. Email: 2601580399@qq.com
Corresponding Author
Xiaoshan Yan
Available Online 22 November 2024.
DOI
10.2991/978-94-6463-570-6_117How to use a DOI?
Keywords
variational modal decomposition; support vector machine regression; express business volume; combined forecasting
Abstract

With the booming development of the tertiary and e-commerce industries, the volume of business in the express delivery industry also maintains a high growth rate. In order to further improve the prediction accuracy of regional express business volume and provide more accurate data support to the local government and express enterprises, this paper follows the idea of "decomposition followed by integration" modelling and proposes a combined prediction model based on variational modal decomposition (VMD) and grey wolf optimization (GWO) support vector regression (SVR), and analyzes the monthly data of the express business volume in Liaoning Province as an example. Furthermore, the combination of the prediction model and the other four models to do a comparison test, the results show that the prediction accuracy of the model is significantly higher than other comparative models, can effectively solve the non-linear and seasonal regional express business volume, and more accurately predict the trend of changes in the regional express business volume.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024)
Series
Advances in Economics, Business and Management Research
Publication Date
22 November 2024
ISBN
978-94-6463-570-6
ISSN
2352-5428
DOI
10.2991/978-94-6463-570-6_117How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xiaoshan Yan
AU  - Yanbin Wang
AU  - Jingwei Zhang
PY  - 2024
DA  - 2024/11/22
TI  - Regional Express Business Volume Forecasting Based on Combinatorial Modelling
BT  - Proceedings of the 2024 5th International Conference on Management Science and Engineering Management (ICMSEM 2024)
PB  - Atlantis Press
SP  - 1168
EP  - 1177
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-570-6_117
DO  - 10.2991/978-94-6463-570-6_117
ID  - Yan2024
ER  -