Retail Sales Forecasting Using Deep Learning: Systematic Literature Review

2022
Linda Eglite, Ilze Birzniece

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.

Keywords
Deep Learning | Sales Forecasting | Retail
DOI
10.7250/csimq.2022-30.03
Hyperlink
https://csimq-journals.rtu.lv/article/view/csimq.2022-30.03/2949

Eglite, L., Birzniece, I. Retail Sales Forecasting Using Deep Learning: Systematic Literature Review. Complex Systems Informatics and Modeling Quarterly, 2022, No. 30, pp. 53-62. ISSN 2255-9922. Available from: doi:10.7250/csimq.2022-30.03

Publication language
English (en)