A novel combination machine learning model for regional GDP prediction: evidence from China

DOI

10.2991/978-94-6463-262-0_101How to use a DOI?

Keywords

Regional GDP; Linear Regression; XGBoost Regression; Entropy method

Abstract

In recent years, the regional GDP prediction has become an efficient tool to coordinate economic development. This paper aims to study the regional GDP prediction and build a novel machine learning model with taking the entropy method into consideration to predict the future GDP values. This research uses the entropy method to calculate weights of the linear regression model and XGBoost regression model, then build a novel combination model to predict the GDP value of different regions. The model will combine the advantages of trend prediction from linear regression models and high fitting accuracy from XGBoost regression models. In the empirical analysis, the paper used the China’s GDP values of different provinces in mainland and built the novel combination model with entropy method, linear regression model and XGBoost regression model. The results reveal that the proposed novel combination model outperforms the tow base models on the mean absolute percentage error evaluation metric.

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.

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TY  - CONF
AU  - Yinghan Xia
PY  - 2023
DA  - 2023/10/09
TI  - A novel combination machine learning model for regional GDP prediction: evidence from China
BT  - Proceedings of the 3rd International Conference on Management Science and Software Engineering (ICMSSE 2023)
PB  - Atlantis Press
SP  - 985
EP  - 993
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-262-0_101
DO  - 10.2991/978-94-6463-262-0_101
ID  - Xia2023
ER  -