Have a personal or library account? Click to login
Application Of Artificial Neural Network Model To Forecast Growth Rate Of Service Sector’s Value In Ho Chi Minh City Cover

Application Of Artificial Neural Network Model To Forecast Growth Rate Of Service Sector’s Value In Ho Chi Minh City

Open Access
|Apr 2025

References

  1. Aaron Kreiner (2019). Can Machine Learning on Economic Data Better Forecast the Unemployment Rate?. Oberlin College and Nomura Securities, May 17, 2019.
  2. Alamsyah, A., & Permana, M. F. (2018, August). Artificial neural network for predicting indonesian economic growth using macroeconomics indicators. In 2018 International Symposium on Advanced Intelligent Informatics (SAIN) (pp. 15-19). IEEE.
  3. Al-Qahtani, F. H., & Crone, S. F. (2013, August). Multivariate k-nearest neighbour regression for time series data—A novel algorithm for forecasting UK electricity demand. In The 2013 international joint conference on neural networks (IJCNN) (pp. 1-8). IEEE.
  4. Ban, T., Zhang, R., Pang, S., Sarrafzadeh, A., & Inoue, D. (2013). Referential k NN regression for financial time series forecasting. In Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part I 20 (pp. 601-608). Springer Berlin Heidelberg.
  5. Bertsimas, D., Delarue, A., & Pauphilet, J. (2021). Prediction with missing data. stat, 1050, 7.
  6. Ćetković, J., Knežević, M., & Žarković, M. (2022). Use of ANN model in economies. In E3S Web of Conferences (Vol. 363, p. 04058). EDP Sciences.
  7. Diebold, F. X., & Nason, J. A. (1990). Nonparametric exchange rate prediction?. Journal of international Economics, 28(3-4), 315-332.
  8. Fraz, T. R., & Fatima, S. (2020). Are Neural Network Models Truly Effective at Forecasting? An Evaluation of Forecast Performance of Traditional Models with Neural Network Model for the Macroeconomic Data of G-7 Countries. International Journal of Economic and Environmental Geology, 11(3), 49-52.
  9. General Statistics Office of Vietnam (2023). Statiscal Yearbook of Viet Nam 2023. Retrieved from https://www.gso.gov.vn
  10. Herbrich, R., Keilbach, M., Graepel, T., Bollmann-Sdorra, P., & Obermayer, K. (1999). Neural networks in economics: Background, applications and new developments. In Computational techniques for modelling learning in economics (pp. 169-196). Boston, MA: Springer US.
  11. Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International journal of forecasting, 20(1), 5-10. https://doi.org/10.1016/j.ijforecast.2003.09.015
  12. Huang, W., Lai, K. K., Nakamori, Y., Wang, S., & Yu, L. (2007). Neural networks in finance and economics forecasting. International Journal of Information Technology & Decision Making, 6(01), 113-140.
  13. Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
  14. Jackie D. Urrutia, Alsafat M. Abdul and Jacky Boy E. Atienza (2019b). Forecasting Philippines imports and exports using Bayesian artificial neural network and autoregressive integrated moving average. AIP Conference Proceedings 2192, 090015 (2019).
  15. Jackie D. Urrutia, Paul Ryan A. Longhas, and Francis Leo T. Mingo (2019a). Forecasting the Gross Domestic Product of the Philippines using Bayesian artificial neural network and autoregressive integrated moving average. AIP Conference Proceedings (2019).
  16. Kayakuş, M., Erdoğan, D., & Terzioğlu, M. (2023). Predicting the share of tourism revenues in total exports. Alphanumeric Journal, 11(1), 17-30.
  17. Krispin, R. (2019). Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R. Packt Publishing Ltd.
  18. Lashgari, A. (2023). Harnessing the Potential of Volatility: Advancing GDP Prediction. arXiv preprint arXiv:2307.05391.
  19. Morris, W., Correa, A., & Leiva, R. (2023). Impact of COVID-19 containment measures on unemployment: A multi-country analysis using a difference-in-differences framework. International Journal of Health Policy and Management, 12.
  20. Mourtas, S. D., Drakonakis, E., & Bragoudakis, Z. (2023). Forecasting the gross domestic product using a weight direct determination neural network. AIMS Math, 8, 24254-24273.
  21. Munka, S., & Kyryliuk, Y. (2022). Forecasting Indicators of Economic Development of Ukraine Using an Artificial Neural Network. Path of Science, 8(1), 3018-3024.
  22. National Center for Socio-Economic Information and Forecasting (2016), Project “Research on Medium-Term Socio-Economic Forecasting Methods in the Russian Federation and Their Applicability in Vietnam”.
  23. N’emeth, K., & Hadházi, D. (2023). GDP nowcasting with artificial neural networks: How much does long-term memory matter?. arXiv preprint arXiv:2304.05805.
  24. Önder, E., Bayır, F., & Hepsen, A. (2013). Forecasting macroeconomic variables using artificial neural network and traditional smoothing techniques. Journal of Applied Finance and Banking, 3(4), 73-104.
  25. Rodríguez-Vargas, A. (2020). Forecasting Costa Rican inflation with machine learning methods. Latin American Journal of Central Banking, 1(1-4), 100012.
  26. Shi, J. (2024). Establishment of Economic Analysis Model Based on Artificial Intelligence Technology. International Journal of Advanced Computer Science & Applications, 15(5).
  27. Stone, C. J. (1977). Consistent nonparametric regression. The annals of statistics, 595-620.
  28. Thomas R. Cook and Aaron Smalter Hall (2017), Macroeconomic Indicator Forecasting with Deep Neural Networks. Kcfred Research Working Paper, September 2017, ISSN 1936-5330.
  29. Ugulava, G. (2019). USE OF ARTIFICIAL NEURAL NETWORKS TO PREDICT TERRITORIAL ECONOMIC INDICATORS. Globalization and Business, 4(8), 143-146.
  30. Wauters, M., & Vanhoucke, M. (2017). A nearest neighbour extension to project duration forecasting with artificial intelligence. European Journal of Operational Research, 259(3), 1097-1111.
  31. Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., ... & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and information systems, 14, 1-37.
  32. Zhang, Q., Yan, L., Hu, R., Li, Y., & Hou, L. (2022). Regional economic prediction model using backpropagation integrated with bayesian vector neural network in big data analytics. Computational Intelligence and Neuroscience, 2022(1), 1438648.
  33. Zhang, Q., Yan, L., Hu, R., Li, Y., & Hou, L. (2022). Regional economic prediction model using backpropagation integrated with bayesian vector neural network in big data analytics. Computational Intelligence and Neuroscience, 2022(1), 1438648.
Language: English
Page range: 60 - 70
Published on: Apr 12, 2025
Published by: WSB Merito University in Gdansk
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Le Hoang Anh, Nguyen Van Hao, Ho Nguyen Thai Bao, Vo Huynh Hung Thinh, Nguyen Truc Van, published by WSB Merito University in Gdansk
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.