Comparing Gradient Boosting Algorithms to Forecast Sales in Retail

  • Ana Clara Chaves Sousa Universidade Federal da Paraíba (UFPB)
  • Thaís Gaudêncio do Rêgo Universidade Federal da Paraíba (UFPB)
  • Yuri de Almeida Malheiros Barbosa Universidade Federal da Paraíba (UFPB)
  • Telmo de Menezes e Silva Filho University of Bristol


The availability of data and the increased processing power of computers have made it easier to make decisions based on data, specially with Artificial Intelligence. One area where AI is widely applicable in companies is Supply Chain Management, particularly in demand forecasting. This paper aims to forecast sales for a company in the Cosmetic, Fragrance, and Toiletry market. Data from 2019 to 2023 were used from two different sales channel. To predict the demand, three Gradient Boosting algorithms (CatBoost, LightGBM, and XGBoost) were compared, and forecasts were made for three different time horizons (next period, five and ten periods ahead). After the experiments, LightGBM showed more stability compared to the other models.

Palavras-chave: Machine Learning, Forecasting, Gradient Boosting


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SOUSA, Ana Clara Chaves; DO RÊGO, Thaís Gaudêncio; BARBOSA, Yuri de Almeida Malheiros; MENEZES E SILVA FILHO, Telmo de. Comparing Gradient Boosting Algorithms to Forecast Sales in Retail. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 596-609. ISSN 2763-9061. DOI: