Abstract
GDP is a convincing indicator measuring comprehensive national strength. It is crucial since the industrial structure, living standards, and consumption level are closely related to GDP. In recent years, the Chinese GDP has maintained rapid growth. Admittedly, the contribution of agriculture to GPD is gradually decreasing. As the foundation of life, the structure of agricultural production still needs to be improved. Therefore, this paper applies machine learning skills to investigate how to improve the agricultural production structure to promote GDP. A total of 47 agricultural products were selected and analyzed. We extracted the production data from 1980 to 2018. K-means clustering model was used to group products into several clusters. The Holt-winters model predicts the following year’s production of the different agriculture products to simulate next year’s GDP. The linear regression model quantifies the relationship between clusters and GDP. Based on that relationship, we provide suggestions on stimulating GDP growth. For assessment, both linear regression and neural network models are used to simulate the GDP after considering the recommendations. Results show that the proposed approach offers relevant recommendations to stimulate the Chinese GDP based on the agriculture data.
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References
Anwar, H., Khan, A.Q.: Relationship between agriculture and GDP growth rates in Pakistan: an econometric analysis (1961–2007). Academic Research International 1, no. 2, p. 322 (2011)
Azlan, A., Yusof, Y., Mohsin, M.F.M.: Univariate financial time series prediction using clonal selection algorithm. Int. J. Adv. Sci. Eng. Inf. Technol. 10(1), 151–156 (2020)
Yoon, J.: Forecasting of real GDP growth using machine learning models: gradient boosting and random forest approach. Comput. Econ. 57(1), 247–265 (2021). https://doi.org/10.1007/s10614-020-10054-w
Shih, H., Rajendran, S.: Comparison of time series methods and machine learning algorithms for forecasting Taiwan blood services foundation’s blood supply. J. Healthcare Eng. 2019 (2019)
Kira, A.R.: The factors affecting Gross Domestic Product (GDP) in developing countries: the case of Tanzania (2013)
Ifa, A., Guetat, I.: Does public expenditure on education promote Tunisian and Moroccan GDP per capita? ARDL approach. J. Finan. Data Sci. 4(4), 234–246 (2018)
Liu, Z., et al.: Machine learning approaches to investigate the relationship between genetic factors and autism spectrum disorder. In: 2021 The 4th International Conference on Machine Learning and Machine Intelligence, pp. 164–171 (2021)
Dou, W., et al.: An AutoML Approach for Predicting Risk of Progression to Active Tuberculosis based on Its Association with Host Genetic Variations (2021)
Kong, L.-W., Fan, H.-W., Grebogi, C., Lai, Y.-C.: Machine learning prediction of critical transition and system collapse. Phys. Rev. Res. 3(1), 013090 (2021)
Xu, J., Lange, K.: Power k-means clustering. In: International Conference on Machine Learning, pp. 6921–6931. PMLR (2019)
Yi, Y.: The relationship between industrial structure and economic growth in China—an empirical study based on panel data. In: E3S Web of Conferences, vol. 275, p. 01009. EDP Sciences (2021)
Canela, M.Á., Alegre, I., Ibarra, A.: Holt-winters forecasting. In: Quantitative Methods for Management, pp. 121-128. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17554-2_13
Aggarwal, C.C.: Neural Networks and Deep Learning. Springer, vol. 10, pp. 978–3 (2018). https://doi.org/10.1007/978-3-319-94463-0
Dinov, Ivo D. K-Means Clustering. In: Data Science and Predictive Analytics, pp. 443-473. Springer, Cham, 2018. https://doi.org/10.1007/978-3-319-72347-1_13
Shahapure, K.R., Nicholas, C.: Cluster quality analysis using silhouette score. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 747–748. IEEE (2020)
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Zhenghan, N., Dib, O. (2023). Agriculture Stimulates Chinese GDP: A Machine Learning Approach. In: Tang, L.C., Wang, H. (eds) Big Data Management and Analysis for Cyber Physical Systems. BDET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-031-17548-0_3
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DOI: https://doi.org/10.1007/978-3-031-17548-0_3
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