Repositorio Institucional Universidad EAFIT :: Examinando por Materia "INTELIGENCIA ARTIFICIAL

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Modelos de aprendizaje automático para el pronóstico de la demanda en eventos promocionales en el comercio electrónico : un estudio aplicado a ventas minoristas
(Universidad EAFIT, 2026-02-05) Duque Giraldo, Tomás; Fonseca Valero, Diego Fernando
The growing importance of e-commerce has intensified the need for accurate demand forecasts, especially during promotional events that generate abnormal consumption peaks such as Black Friday or Christmas. These events lead to sudden increases in sales that traditional statistical methods fail to anticipate adequately. As a result, stock shortages or overstocking may occur. To address this problem, this paper applies advanced machine learning techniques capable of incorporating nonlinear patterns and contextual variables, with the aim of improving the accuracy of demand estimates in such scenarios. In particular, a tree-based machine learning predictive model known as LightGBM is evaluated against traditional forecasting methods, using a public dataset of US retail sales. This identifies the most effective approach under conditions of high volatility and examines its relevance during holiday and promotional periods when consumer behavior changes significantly.