Resumen
This systematic literature review examines the deep learning (DL) models for retail sales forecast. The accuracy of a retail sales forecast is a prevalent force for uninterrupted business operations. Accuracy for retailers means limiting supply chain and storage costs, ensuring no product is out of stock, and facilitating smooth promotional operations. The study analyses the DL frameworks used in reviewed literature. Tested DL models are listed, as well as other machine learning and linear models used for the evaluation comparison. Additionally, the review presents the metrics used by the authors for the model evaluation. This article concludes by describing the benefits and limitations of DL models for sales forecasting.