Gross Domestic Product Prediction in Various Countries with Classic Machine Learning Techniques-Reference-Cited by-同舟云学术

Gross Domestic Product Prediction in Various Countries with Classic Machine Learning Techniques

Published:2023 Issue: Volume: Page:136-147
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Tran Chi Le Hoang,Phan Trang Huyen,Thi-Ngoc-Diem Pham,Nguyen Hai Thanh

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-28790-9_9

Reference19 articles.

1. Laygo-Matsumoto, S., Samonte, M.J.: Philippine economic growth: GDP prediction using machine learning algorithms. In: 2021 4th International Conference on Computing and Big Data, ICCBD 2021, pp. 15–20. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3507524.3507526

2. Magazzino, C., Mele, M., Schneider, N.: A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions. Renew. Energy 167, 99–115 (2021). https://doi.org/10.1016/j.renene.2020.11.050. https://www.sciencedirect.com/science/article/pii/S0960148120317936

3. Richardson, A., van Florenstein Mulder, T., Vehbi, T.: Nowcasting GDP using machine-learning algorithms: a real-time assessment. Int. J. Forecasting 37(2), 941–948 (2021). https://www.sciencedirect.com/science/article/pii/S016920702030159X

4. Wochner, D.: Dynamic factor trees and forests - a theory-led machine learning framework for non-linear and state-dependent short-term U.S. GDP growth predictions (2020)

5. Lv, H.: Chinese and American GDP forecasts based on machine learning. World Sci. Res. J. 6(6), 95–104 (2020)

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