I’m still here to forecast: a comparison between SARIMA and SARIMAX for seasonal poultry sales in Brazil

Authors

DOI:

https://doi.org/10.20397/2177-6652/2025.v25i3.3199

Keywords:

Séries Temporais, SARIMA, SARIMAX, Sazonalidade.

Abstract

Objective: This study aims to compare the application of SARIMA and SARIMAX econometric models in forecasting the demand for seasonal products, using real monthly sales data of frozen poultry from two competing brands in Brazil.

Methodology: The methodology is based on time series modeling using SARIMA and SARIMAX models. The approach involved splitting the dataset into training and testing sets, a procedure widely adopted in empirical studies focused on out-of-sample forecasting, to assess model robustness, in line with the principles of machine learning validation techniques.

Originality/Relevance: The originality of this research lies in the application of these models to forecast holiday-season poultry demand in a Brazilian company—a niche rarely explored in national literature—while also incorporating an analysis with an exogenous variable representing direct market competition. The study contributes by bridging traditional time series practices with predictive logic inspired by machine learning.

Main Results: The results demonstrated superior performance of the SARIMA model, with lower forecasting errors and greater generalization capability, even without the use of external variables.

Theoretical/Methodological Contributions: The research reinforces the applicability of seasonal models in markets characterized by strong cyclicality and shows that, in certain contexts, statistical simplicity can outperform multivariate complexity, even in volatile and competitive environments.

Author Biographies

Edimilson Costa Lucas, Universidade Presbiteriana Mackenzie (PPGCFTG)

Doutor em Administração de Empresas (linha de Finanças) pela EAESP/FGV. Mestre em Estatística pela UNICAMP. MBA em Finanças pela FGV. Bacharel em Matemática pela UFU. Professor do programa de pós-graduação (stricto sensu) em Controladoria, Finanças e Tecnologias em Gestão na Universidade Presbiteriana Mackenzie. Professor do Departamento de Ciências Atuariais da EPPEN/UNIFESP.

Adilson Carlos Yoshikuni, Universidade Presbiteriana Mackenzie (PPGCFTG)

Pós-doutor e doutor em Administração de Empresas pela FGV-EAESP (2018,2015), Mestre em Ciências Contábeis e Atuariais pela PUC-SP (2005), Pós-graduado em MBA Executivo Internacional pela FGV-EBAPE e University of California Irvine- EUA (2005), Bacharel em Ciência da Computação (1995) e Análise de Sistema (1993) pela Universidade Paulista. Na Universidade Presbiteriana Mackenzie é professor integral do programa de Mestrado e de Doutorado Profissional em Controladoria, Finanças e Tecnologias de Gestão Empresariais (PPGCFTG), e coordenador do grupo de pesquisa em Technology Analytics in Management, Accounting and Finance (TAMAF), professor convidado do Instituto de Desenvolvimento Educacional IDE da FGV.

Carlos Alberto Di Agustini, Strong Business School

Doutor em engenharia de produção, mestre em administração e especialista em finanças pela New York University (Stern) e University of California (UCLA). Foi CEO e diretor estatutário de empresa financeira da Volkswagen, executivo do Banco Caterpillar, Banco Itaú e Grupo Ultra. É autor de artigos e livros na área de finanças. Professor convidado da FGV, professor do Instituto Mauá de Tecnologia, professor pesquisador da Strong Business School e professor da USCS. Atua como membro de conselho de administração.

Vinícius Augusto Brunassi Silva, FECAP, Programa de Mestrado Profissional em Administração – Finanças.

Doutor e Mestre em Administração de Empresas (linha de Finanças) pela FGV-EAESP. Professor e Pesquisador da FECAP no programa de mestrado profissional em Administração (Finanças).

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Published

2025-06-30

How to Cite

Costa Lucas, E., Carlos Yoshikuni, A., Di Agustini, C. A., & Augusto Brunassi Silva, V. (2025). I’m still here to forecast: a comparison between SARIMA and SARIMAX for seasonal poultry sales in Brazil. Revista Gestão & Tecnologia, 25(3), 98–118. https://doi.org/10.20397/2177-6652/2025.v25i3.3199

Issue

Vol. 25 No. 3 (2025)

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ARTIGO

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