GDP Forecasting for Representative Cities in Jiangsu Province: A Comparative Study of Multiple Linear Regression and Random Forest

Chenxuan Huang

doi:10.54254/2755-2721/2025.LD28670

Applied and Computational EngineeringOpen access

GDP Forecasting for Representative Cities in Jiangsu Province: A Comparative Study of Multiple Linear Regression and Random Forest

Research Article

Open Access

GDP Forecasting for Representative Cities in Jiangsu Province: A Comparative Study of Multiple Linear Regression and Random Forest

Chenxuan Huang ^1*

¹ Jinling High School Hexi Campus, Nanjing, 210000, China

^*Corresponding author: H13776636184@outlook.com

Published on 28 October 2025

ACE Vol.202

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-497-7

ISBN (Online): 978-1-80590-498-4

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Abstract

Gross domestic product (GDP) is the sum of the market value of final goods and services produced by all permanent units in a country over a specific period. It is a core indicator for measuring economic scale. Regional GDP forecasting is also an important and indispensable task. Jiangsu Province is one of the major economic powerhouse in China, accurate regional GDP forecasting is crucial for guiding government policy-making, optimizing resource allocation, and addressing inter-city economic disparities. This study focuses on 8 representative prefecture-level cities in Jiangsu (covering Southern, Central, and Northern Jiangsu) and utilizes panel data from 2005 to 2023, including municipal GDP figures and 16 key economic indicators (e.g., fixed asset investment, local fiscal revenue, and total patent applications). Two models—multiple linear regression (MLR) and random forest (RF)—are constructed to forecast GDP. The essay finds that the multivariate linear regression model outperforms the random forest model in predicting GDP, achieving a closer approximation to the true value, provides data support for the government.

Keywords:

Linear Regression, Random Forest, GDP Prediction, Jiangsu

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References

[1]. Guo, H., Suo, L., & Yang, F. (2024). Analyzing the industry influencing factors of China’s GDP based on a graph model. Commercial Observation, 10(23), 29–33.

[2]. Cohen Kaminitz, S. (2023). The significance of GDP: a new take on a century-old question. Journal of Economic Methodology, 30(1), 1–14. https: //doi.org/10.1080/1350178X.2023.2167228

[3]. Li, W. (2022). Between language and image: Research on the communication effects of data visualization based on dual coding theory [Master’s thesis, South China University of Technology]. https: //doi.org/10.27151/d.cnki.ghnlu.2022.004637

[4]. Ma, J., Li, S., & Xia, M. (2022). A review of the application of machine learning in GDP prediction. Technology Intelligence Research, 4(3), 73–94. https: //doi.org/10.19809/j.cnki.kjqbyj.2022.03.008

[5]. Liu, B., Liu, Z., Liu, Y., et al. (2021). A review of data visualization research. Journal of Hebei University of Science and Technology, 42(6), 643–654.

[6]. Artekin, A. Ö., & Kalayci, S. (2024). Comparative analysis of Gini coefficient, GDP, energy consumption, and transportation modes on CO2 using NARDL (Nonlinear Distributed Lag Autoregressive Model) for the USA. Sustainability, 16(20), 9030. https: //doi.org/10.3390/su16209030

[7]. Ma, W. (2022). An empirical study on RMB exchange rate forecasting based on a combined model. https: //doi.org/10.27835/d.cnki.gnjsj.2022.000389

[8]. Guan, M. (2025). Analysis of the application of corporate financial data visualization in the context of big data. China Market, (19), 147–150. https: //doi.org/10.13939/j.cnki.zgsc.2025.19.037

[9]. Guan, Y. (2021). Factors influencing Henan Province’s GDP based on multiple linear regression. Rural Economy and Technology, 32(5), 221–224.

[10]. Wang, P. (2020). Stock price analysis and prediction based on multiple linear regression. Technology and Economic Market, (1), 84–85.

[11]. Xie, F., Sun, J., Yu, M., Lv, J., & Ma, H. (2022). Spatialization of GDP in Lanzhou City based on Luojia-1 and random forest. Remote Sensing Information, 37(2), 53–59.

[12]. Zhao, Y., Lou, F., & Cheng, Y. (2024). Construction of a macroeconomic leading indicator system based on random forest algorithm. Survey World, (4), 3–15. https: //doi.org/10.13778/j.cnki.11-3705/c.2024.04.001

[13]. Zhao, J., Li, Y., Wang, D., & Zhang, J.(2021). Heart disease prediction algorithm based on optimized random forest. Journal of Qingdao University of Science and Technology (Natural Science Edition), 42(2), 112–118. https: //doi.org/10.16351/j.1672-6987.2021.02.016

[14]. James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Linear regression. In An introduction to statistical learning. Springer Texts in Statistics. Springer, Cham. https: //doi.org/10.1007/978-3-031-38747-0_3

References

[1]. Guo, H., Suo, L., & Yang, F. (2024). Analyzing the industry influencing factors of China’s GDP based on a graph model. Commercial Observation, 10(23), 29–33.

[2]. Cohen Kaminitz, S. (2023). The significance of GDP: a new take on a century-old question. Journal of Economic Methodology, 30(1), 1–14. https: //doi.org/10.1080/1350178X.2023.2167228

[5]. Liu, B., Liu, Z., Liu, Y., et al. (2021). A review of data visualization research. Journal of Hebei University of Science and Technology, 42(6), 643–654.

[7]. Ma, W. (2022). An empirical study on RMB exchange rate forecasting based on a combined model. https: //doi.org/10.27835/d.cnki.gnjsj.2022.000389

[9]. Guan, Y. (2021). Factors influencing Henan Province’s GDP based on multiple linear regression. Rural Economy and Technology, 32(5), 221–224.

[10]. Wang, P. (2020). Stock price analysis and prediction based on multiple linear regression. Technology and Economic Market, (1), 84–85.

[11]. Xie, F., Sun, J., Yu, M., Lv, J., & Ma, H. (2022). Spatialization of GDP in Lanzhou City based on Luojia-1 and random forest. Remote Sensing Information, 37(2), 53–59.

Cite this article

Huang,C. (2025). GDP Forecasting for Representative Cities in Jiangsu Province: A Comparative Study of Multiple Linear Regression and Random Forest. Applied and Computational Engineering,202,56-64.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-497-7(Print) / 978-1-80590-498-4(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/london.html

Conference date: 12 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.202

ISSN: 2755-2721(Print) / 2755-273X(Online)

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