[
Aaron Kreiner (2019). Can Machine Learning on Economic Data Better Forecast the Unemployment Rate?. Oberlin College and Nomura Securities, May 17, 2019.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Bertsimas, D., Delarue, A., & Pauphilet, J. (2021). Prediction with missing data. stat, 1050, 7.
]Search in Google Scholar
[
Ćetković, J., Knežević, M., & Žarković, M. (2022). Use of ANN model in economies. In E3S Web of Conferences (Vol. 363, p. 04058). EDP Sciences.
]Search in Google Scholar
[
Diebold, F. X., & Nason, J. A. (1990). Nonparametric exchange rate prediction?. Journal of international Economics, 28(3-4), 315-332.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
General Statistics Office of Vietnam (2023). Statiscal Yearbook of Viet Nam 2023. Retrieved from https://www.gso.gov.vn
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
]Search in Google Scholar
[
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).
]Search in Google Scholar
[
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).
]Search in Google Scholar
[
Kayakuş, M., Erdoğan, D., & Terzioğlu, M. (2023). Predicting the share of tourism revenues in total exports. Alphanumeric Journal, 11(1), 17-30.
]Search in Google Scholar
[
Krispin, R. (2019). Hands-On Time Series Analysis with R: Perform time series analysis and forecasting using R. Packt Publishing Ltd.
]Search in Google Scholar
[
Lashgari, A. (2023). Harnessing the Potential of Volatility: Advancing GDP Prediction. arXiv preprint arXiv:2307.05391.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Munka, S., & Kyryliuk, Y. (2022). Forecasting Indicators of Economic Development of Ukraine Using an Artificial Neural Network. Path of Science, 8(1), 3018-3024.
]Search in Google Scholar
[
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”.
]Search in Google Scholar
[
N’emeth, K., & Hadházi, D. (2023). GDP nowcasting with artificial neural networks: How much does long-term memory matter?. arXiv preprint arXiv:2304.05805.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Rodríguez-Vargas, A. (2020). Forecasting Costa Rican inflation with machine learning methods. Latin American Journal of Central Banking, 1(1-4), 100012.
]Search in Google Scholar
[
Shi, J. (2024). Establishment of Economic Analysis Model Based on Artificial Intelligence Technology. International Journal of Advanced Computer Science & Applications, 15(5).
]Search in Google Scholar
[
Stone, C. J. (1977). Consistent nonparametric regression. The annals of statistics, 595-620.
]Search in Google Scholar
[
Thomas R. Cook and Aaron Smalter Hall (2017), Macroeconomic Indicator Forecasting with Deep Neural Networks. Kcfred Research Working Paper, September 2017, ISSN 1936-5330.
]Search in Google Scholar
[
Ugulava, G. (2019). USE OF ARTIFICIAL NEURAL NETWORKS TO PREDICT TERRITORIAL ECONOMIC INDICATORS. Globalization and Business, 4(8), 143-146.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
