aspg-1-1756-1791419706 20261008003506 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group American Journal of Business and Operations Research AJBOR 2770-0216 2692-2967 2023 10 2 Irina V. Pustokhina Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, Moscow 117997, Russia Denis A. Pustokhin Department of Logistics, State University of Management , Moscow 109542, This paper presents a comparative analysis of traditional forecasting methods and machine learning (ML) techniques for sales prediction in e-commerce. We first review the literature on both traditional and ML methods for sales prediction in e-commerce, highlighting their strengths and weaknesses. The study uses a dataset of daily sales from an e-commerce retailer to conduct a comprehensive empirical study thar compares the performance of literature methods from both categories. The analysis considers different forecasting horizons and evaluates the accuracy of the predictions using various performance metrics, such as mean absolute error and mean squared error. The study finds that ML techniques generally outperform traditional methods, especially for longer forecasting horizons. However, some traditional methods, such as the Holt-Winters method, can also perform well under certain conditions. Our study provides insights into the relative strengths and weaknesses of traditional and ML methods for sales prediction in e-commerce and can guide practitioners in selecting appropriate methods for their specific requirements. 2023 39 51 1756 https://creativecommons.org/licenses/by/4.0/ 10.54216/AJBOR.100205 https://www.americaspg.com/journal/1/article/1756 https://www.americaspg.com/storage/71702491380.pdf