Development of machine learning based demand forecasting models for the e-commerce sector

Authors : Alim Toprak Fırat, Onur Aygün, Mustafa Göğebakan, Mehmet Fatih Akay, Ceren Ulus

Pages : 13-20

Doi:10.70669/ijedt.1567739

View : 128 | Download : 142

Publication Date : 2025-08-20

Article Type : Research Paper

Abstract :The e-commerce sector has undergone rapid and dynamic growth in recent years. For companies aspiring to lead in this competitive industry, it is crucial to efficiently and cost-effectively respond to evolving consumer demands. In this context, the ability to accurately forecast future product demand becomes imperative. This study aims to develop forecasting models utilizing machine learning-based techniques, specifically Multi-Layer Perceptron (MLP), Multi-Horizon Quantile Recurrent Neural Network (MQRNN), and Random Forest (RF), to predict future product demand. The demand forecasting models were developed for the months of July and August, based on daily sales data for Fast-Moving Consumer Goods (FMCG) products spanning from January 1, 2023, to August 25, 2024. The models’ performances were evaluated using Mean Absolute Percentage Error (MAPE). Upon examining the forecasting models developed using MLP, MQRNN, and RF, it has been observed that MQRNN exhibited the superior performance.
Keywords : E-Ticaret, Talep Tahmini, Makine Öğrenimi