Review of Artificial Intelligence Models for Constructing a Sales Forecasting Module to Enhance Decision-Making

Aguilar-Alonso I.; Espinoza Espinoza J. · Communications in Computer and Information Science (2025) · DOI: 10.1007/978-3-031-77493-5_13

Resumen

In today’s rapidly evolving business landscape, making informed strategic decisions is paramount for future success. Forecasting, a widely adopted practice across diverse domains, empowers us to predict uncertain future events and utilize existing data to project potential outcomes. This article aims to evaluate models that enhance decision-making, projection, and model combination. Through a concise bibliographic review of prestigious journal articles from the past five years, 97 relevant papers were identified, with 30 selected for detailed analysis. The key finding is that leveraging projections can significantly enhance decision-making by anticipating uncertain events in a company’s future. Notably, the ARIMA and LSTM models emerge as standout choices for achieving this goal. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Instituciones: Universidad Nacional Tecnológica de Lima Sur