Thematic Section - Future of Energy-efficient Operations and Production Systems

A comparative study of forecasting methods using real-life econometric series data

Cláudia Eliane da Matta; Natália Maria Puggina Bianchesi; Milena Silva de Oliveira; Pedro Paulo Balestrassi; Fabiano Leal

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Paper aims: This paper presents a comparative evaluation of different forecasting methods using two artificial neural networks (Multilayer Perceptron network and Radial Basis Functions Neural Network) and the Gaussian process regression.

Originality: Due to the current world scenario, solving economic problems has become extremely important. Artificial neural networks are one of the most promising tools to forecast economic trends and are being widely studied in economic analyses. Therefore, due to the concerns about the performance of different forecasting methods to solve economic problems, this study contributes with an example of the forecasting performance of artificial neural network models compared with Gaussian process regression using Nelson-Plosser and U.S. macroeconomic real-life data sets.

Research method: Two real-life data sets were used to evaluate the forecasting methods proposed in this paper. These data sets were normalised to values between zero and one. After that, the data training was performed and, once it was built, a model was used to generate forecasts. Thus, observations were made to verify how accurately the fitted model forecast the values.

Main findings: The results obtained from the study show that, for all forecasting horizons, multi-layer perceptron networks and Gaussian process regression models had the most satisfactory results. On the other hand, the radial basis functions neural network model was unsuitable for econometric data.

Implications for theory and practice: This study contributes to a discussion about artificial neural networks and Gaussian process regression models for econometric forecasting. Although artificial neural networks are mainly used in economic analyses, the results showed that not all models, such as radial basis functions neural networks, present good results. In addition, the regression of the Gaussian process showed promising results to forecast econometric data.


Artificial neural networks, Gaussian process regression, Forecasting, Macroeconomic series


Ballabio, C., Lugato, E., Fernandez-Ugalde, O., Orgiazzi, A., Jones, A., Borrelli, P., Montanarella, L., & Panagos, P. (2019). Mapping LUCAS topsoil chemical properties at European scale using Gaussian process regression. Geoderma, 355, 113912. PMid:31798185.

Bandeira, S. G., Alcalá, S. G. S., Vita, R. O., Barbosa, T. M. G. A. (2020). Comparison of selection and combination strategies for demand forecasting methods. Production, 30, e20200009.

Brooks, C., Hoepner, A. G. F., Mcmillan, D., Vivian, A., & Wese Simen, C. (2019). Financial data science: the birth of a new financial research paradigm complementing econometrics? The European Journal of Finance, 25(17), 1627-1636. Retrieved in 2021, May 15, from

Büyükşahin, Ü. Ç., & Ertekin, Ş. (2019). Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing (Amsterdam), 361, 151-163.

Chen, J., Jiang, F., Li, H., & Xu, W. (2016). Chinese stock market volatility and the role of U.S. economic variables. Pacific-Basin Finance Journal, 39, 70-83.

Chen, Z., Lin, X., Xiong, C., & Chen, N. (2020). Modeling the relationship of precipitation and water level using grid precipitation products with a neural network model. Remote Sensing, 12(7), 1096.

dos Santos, C. H., Lima, R. D. C., Leal, F., de Queiroz, J. A., Balestrassi, P. P., & Montevechi, J. A. B. (2020). A decision support tool for operational planning: a Digital Twin using simulation and forecasting methods. Production, 30, e20200018. Retrieved in 2021, May 15, from

Dritsakis, N., & Klazoglou, P. (2018). Forecasting unemployment rates in USA using Box-Jenkins methodology. International Journal of Economics and Financial Issues, 8(1), 9-20. Retrieved in 2021, May 15, from

Feng, X., Ma, G., Su, S.-F., Huang, C., Boswell, M. K., & Xue, P. (2020). A multi-layer perceptron approach for accelerated wave forecasting in Lake Michigan. Ocean Engineering, 211(1-2), 107526.

FRED. (2021). FRED: Federal Reserve Economic Data. St. Louis, MO: Federal Reserve Bank of St. Louis. Retrieved in 2021, May 15, from

Fu, Q., Shen, W., Wei, X., Zheng, P., Xin, H., & Zhao, C. (2019). Prediction of the diet nutrients digestibility of dairy cows using Gaussian process regression. Information Processing in Agriculture, 6(3), 396-406.

Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223-2273.

Gupta, P., Batra, S. S., & Jayadeva, (2017). Sparse short-term time series forecasting models via minimum model complexity. Neurocomputing (Amsterdam), 243, 1-11.

Khraisha, T. (2020). Complex economic problems and fitness landscapes: Assessment and methodological perspectives. Structural Change and Economic Dynamics, 52, 390-407.

Konny, C. (2020). Modernizing data collection for the Consumer Price Index. Business Economics (Cleveland, Ohio), 55(1), 45-52.

Li, Y., Liu, R. W., Liu, Z., & Liu, J. (2019). Similarity grouping-guided neural network modeling for maritime time series prediction. IEEE Access: Practical Innovations, Open Solutions, 7(99), 72647-72659.

Madhiarasan, M., & Deepa, S. (2017). Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting. Artificial Intelligence Review, 48(4), 449-471.

Montgomery, D. C., Jennings, C., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. Hoboken: Wiley.

Nelson, C. R., & Plosser, C. R. (1982). Trends and random walks in macroeconmic time series. Journal of Monetary Economics, 10(2), 139-162.

Nourani, V., Paknezhad, N. J., & Tanaka, H. (2021). Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-based modeling of the hydro-climatic processes, a review. Sustainability, 13(4), 1633.

Puchalsky W., Ribeiro, G. T., da Veiga, C. P., Freire, R. Z., & Santos Coelho, L. (2018). Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimisation: an analysis of the soybean sack price and perishable products demand. International Journal of Production Economics, 203, 174-189.

Qi, M., & Zhang, G. P. (2008). Trend time-series modeling and forecasting with neural networks. IEEE Transactions on Neural Networks, 19(5), 808-816. PMid:18467210.

Rani R, H. J., & Victoire T, A. A. (2018). Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimiser. PLoS One, 13(5), e0196871.

Rasmussen, C. E., & Nickisch, H. (2010). Gaussian Processes for Machine Learning (GPML) Toolbox. Journal of Machine Learning Research, 11, 3011-3015.

Robinson, L., Schulz, J., Ragnedda, M., Pait, H., Kwon, K. H., & Khilnani, A. (2021). An Unequal Pandemic: vulnerability and COVID-19. The American Behavioral Scientist, 1-5.

Sadeghi, G., Pisello, A. L., Nazari, S., Jowzi, M., & Shama, F. (2021). Empirical data-driven multi-layer perceptron and radial basis function techniques in predicting the performance of nanofluid-based modified tubular solar collectors. Journal of Cleaner Production, 295, 126409.

Safari, M.-J.-S., Aksoy, H., & Mohammadi, M. (2016). Artificial neural network and regression models for flow velocity at sediment incipient deposition. Journal of Hydrology (Amsterdam), 541, 1420-1429.

Safi, S. K. (2016). A comparison of artificial neural network and time series models for forecasting GDP in Palestine. American Journal of Theoretical and Applied Statistics, 5(2), 58.

Samadianfard, S., Hashemi, S., Kargar, K., Izadyar, M., Mostafaeipour, A., Mosavi, A., Nabipour, N., & Shamshirband, S. (2020). Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimisation algorithm. Energy Reports, 6, 1147-1159. Retrieved in 2021, May 15, from

Smets, F., & Wouters, R. (2005). Comparing shocks and frictions in US and euro area business cycles: a Bayesian DSGE approach. Journal of Applied Econometrics, 20(2), 161-183.

Sun, S., Lu, H., Tsui, K.-L., & Wang, S. (2019). Nonlinear vector auto-regression neural network for forecasting air passenger flow. Journal of Air Transport Management, 78, 54-62.

Torra, S., & Claveria, O. (2017). Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting (Vol. 201701). Barcelona: Regional Quantitative Analysis Group, University of Barcelona.

Wu, R., & Wang, B. (2018). Gaussian process regression method for forecasting of mortality rates. Neurocomputing (Amsterdam), 316, 232-239.

Yan, J., Li, K., Bai, E., Yang, Z., & Foley, A. (2016). Time series wind power forecasting based on variant Gaussian process and TLBO. Neurocomputing (Amsterdam), 189, 135-144.

Yu, R., Leung, P., & Bienfang, P. (2006). Predicting shrimp growth: artificial neural network versus nonlinear regression models. Aquacultural Engineering, 34(1), 26-32.

Zhang, X., Liu, Y., Yang, M., Zhang, T., Young, A. A., & Li, X. (2013). Comparative study of four time series methods in forecasting typhoid fever incidence in China. PLoS One, 8(5), e63116. PMid:23650546.

Zhang, X., Xue, T., & Eugene Stanley, H. (2019). Comparison of econometric models and artificial neural networks algorithms for the prediction of baltic dry index. IEEE Access: Practical Innovations, Open Solutions, 7(99), 1647-1657.

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