23/Jun/2026
Título
Ensemble of machine learning applied to economic cycles analysis: a comparative study using antecedent macroeconomic indicators for brazilian GDP prediction classification
Autores
- Eduardo Palhares Jr.
- Antonio Marcos Teixeira de Araujo
- Adriano Honorato de Souza
- Noam Gadelha da Silva
- Wenndisson da Silva Souza
Abstract
This work proposes a comparative study between several machine learning techniques, applied in the analysis of the phases of the Brazilian economic cycle. To this end, several macroeconomic indicators were used to build a model that was able to identify and predict the turning points of the economic cycle, such as the beginning of a recession or a recovery. The discretization of the variables proved to be decisive in the quality of the classification process, due to the diversity of the data and the non-linear nature of the analyzed phenomenon. The different techniques used reinforce a dilemma, because usually the best results come from very abstract methods, making it difficult to interpret the steps and their causes.
Keywords: Machine Learning, Classification, Economic Cycle, Multiclass-discretization.
Citation
JUNIOR,E. P.; ARAUJO, A. M. T.; SOUZA, A. H.; SILVA, N. G.;SOUZA, W. S. Ensemble of machine learning applied to economic cycles analysis: a comparative study using antecedent macroeconomic indicators for brazilian GDP prediction classification. R. Bras. Planej. Desenv. Curitiba, v. 15, n. 01, p. 24-48, jan./abr. 2026. Disponível em: <https://periodicos.utfpr.edu.br/rbpd>. Acesso em: 13 de março de 2026.
