2020/09/21 by Resul Tugay, Tugay, Resul, Şule Gündüz Öğüdücü +1
Business, Management and Accounting · Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Customer churn and segmentation #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Stock Market Forecasting Methods #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2009.09756
openalex publication_date 2020/09/21 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Supply and demand are two fundamental concepts of sellers and customers. Predicting demand accurately is critical for organizations in order to be able to make plans. In this paper, we propose a new approach for demand prediction on an e-commerce web site. The proposed model differs from earlier models in several ways. The business model used in the e-commerce web site, for which the model is implemented, includes many sellers that sell the same product at the same time at different prices where the company operates a market place model. The demand prediction for such a model should consider the price of the same product sold by competing sellers along the features of these sellers. In this study we first applied different regression algorithms for specific set of products of one department of a company that is one of the most popular online e-commerce companies in Turkey. Then we used stacked generalization or also known as stacking ensemble learning to predict demand. Finally, all the approaches are evaluated on a real world data set obtained from the e-commerce company. The experimental results show that some of the machine learning methods do produce almost as good results as the stacked generalization method.