Demand Prediction Using Machine Learning Methods and Stacked Generalization

TL;DR

This paper introduces a demand prediction model for e-commerce that incorporates multiple sellers and uses stacked generalization, demonstrating its effectiveness on real-world data from a Turkish online marketplace.

Contribution

The study develops a demand prediction approach using stacking ensemble learning tailored for a multi-seller e-commerce environment, improving prediction accuracy.

Findings

Stacked generalization outperforms individual regression models.

Some machine learning methods achieve comparable results to stacking.

The approach is validated on real-world e-commerce data.

Abstract

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.