Application of Deep Learning Methods in Time Series for the Forecast of the Macroeconomic Situation in Latin America
DOI:
https://doi.org/10.26439/interfases2022.n015.5817Keywords:
Deep Learning, GDP forecasting, CEPAL, Neural NetworkAbstract
Deep learning methods can be applied to generate predictive models. We worked with the gross domestic product (GDP) of six Latin American countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru, using annual and quarterly macroeconomic indicators from the World Bank and the Economic Commission for Latin America and the Caribbean (ECLAC), respectively. For the pre-processing of the data, we decomposed the quarterly series into trend, seasonality, and residual and used them as additional characteristics to provide more information to the models. In addition, outliers resulting from the impact of the COVID-19 pandemic on the world economy were replaced. Multilayer perceptron, convolutional neural networks, LSTM, GRU, and SeqToSeq models were built for each country and their series’ frequency, then evaluated by continuous cross-validation and MAE, RMSE, and MAPE metrics. The optimal models vary for each case.
Downloads
References
Banco Mundial. (2021). Banco de datos. Recuperado el 24 de octubre de 2021, de https://databank.bancomundial.org/
Brownlee, J. (2017). Deep learning for time series forecasting: Predict the future with MLPs, CNNs and LSTMs in Python. Machine Learning Mastery.
Chollet, F. (2018). Deep learning with Python. Manning Publications Co.
Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. J. (1990). STL: A seasonal trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3-33.
Comisión Económica para América Latina y el Caribe. (2021). CEPALSTAT: Bases de Datos y Publicaciones Estadísticas. Recuperado el 14 de diciembre de 2021 de https://statistics.cepal.org/portal/cepalstat/dashboard.html
Cook, T., & Smalter Hall, A. (2017). Macroeconomic indicator forecasting with deep neural networks. The Federal Reserve Bank of Kansas City Research Working Papers. https://doi.org/10.18651/rwp2017-11
Guimarães, R. R. S. (2022). Deep learning macroeconomics [Tesis de maestría, Universidade Federal do Rio Grande do Sul]. http://hdl.handle.net/10183/239533
Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3.ª ed.). Elsevier/Morgan Kaufmann.
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: principles and practice (3.ª ed.), OTexts. Recuperado el 10 de mayo de 2022 de https://OTexts.com/fpp3
Jamieson, K., & Talwalkar, A. (2016). Non-stochastic best arm identification and hyperparameter optimization. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research 51, 240-248. https://proceedings.mlr.press/v51/jamieson16.html
Jung, J.-K., Patnam, M., & Ter-Martirosyan, A. (2018). An algorithmic crystal ball: Forecasts-based on machine learning. IMF Working Papers, 18(230), 1-33. https://doi.org/10.5089/9781484380635.001
Kelany, O., Aly, S., & Ismail, M. A. (2020, November). Deep learning model for financial time series prediction. 2020 14th International Conference on Innovations in Information Technology (IIT), 120-125.
Lazzeri, F. (2020). Machine learning for time series forecasting with Python. Wiley.Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2018). Hyperband: A novel bandit-based approach to hyperparameter optimization. Journal of Machine Learning Research, 18(185), 1-52. http://jmlr.org/papers/v18/16-558.html
Mankiw, N. G. (2014). Macroeconomía, (8.ª ed.). Antoni Bosch.
Nguyen, H. T., & Nguyen, D. T. (2020). Transfer learning for macroeconomic forecasting. 2020 7th NAFOSTED Conference on Information and Computer Science (NICS), 332-337. https://doi.org/10.1109/NICS51282.2020.9335848
Skansi, S. (2018). Introduction to deep learning: From logical calculus to artificial intelligence. Springer.
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS’14), 2, 3104-3112. https://dl.acm.org/doi/10.5555/2969033.2969173
Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. Pearson Education.
Viswanath, S., Saha, M., Mitra, P., & Najundiah R. S. (2019), Deep learning based LSTM and SeqToSeq Models to detect monsoon spells of India. En João M. F. Rodrigues, Pedro J. S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot (Eds.), Computational Science – ICCS 2019 (Part II, vol. 11537, pp. 204-218). https://doi.org/10.1007/978-3-030-22741-8_15
Zyatkov, N., & Krivorotko, O. (2021). Forecasting recessions in the US Economy using machine learning methods. 2021 17th International Asian School-Seminar “Optimization Problems of Complex Systems (OPCS)”, 139-146, https://doi.org/10.1109/OPCS53376.2021.9588678
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under an Attribution 4.0 International (CC BY 4.0) License. that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Last updated 03/05/21
