Demand Prediction Using Machine Learning Methods and Stacked Generalization
This addresses demand forecasting for e-commerce companies with marketplace models, but it is incremental as it applies existing methods to a specific business context.
The paper tackled demand prediction for an e-commerce marketplace with multiple sellers offering the same product at different prices, using regression algorithms and stacked generalization, and found that some machine learning methods performed nearly as well as the ensemble approach on real-world data.
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.