Comparing Gradient Boosting Algorithms to Forecast Sales in Retail - Sousa, Ana Clara Chaves; do Rêgo, Thaís Gaudêncio; Barbosa, Yuri de Almeida Malheiros; Menezes e Silva Filho, Telmo de (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2023: Anais do XX Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2023.234285, 2023)
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.