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Authors: Resul Tugay and Şule Gündüz Öğüdücü

Affiliation: Istanbul Technical University, Turkey

ISBN: 978-989-758-255-4

Keyword(s): Stacked Generalization, Random Forest, Demand Prediction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Statistics Exploratory Data Analysis ; Web Analytics

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 t he 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. (More)

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Paper citation in several formats:
Tugay, R. and Gündüz Öğüdücü, Ş. (2017). Demand Prediction using Machine Learning Methods and Stacked Generalization.In Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-255-4, pages 216-222. DOI: 10.5220/0006431602160222

@conference{data17,
author={Resul Tugay. and Şule Gündüz Öğüdücü.},
title={Demand Prediction using Machine Learning Methods and Stacked Generalization},
booktitle={Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2017},
pages={216-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006431602160222},
isbn={978-989-758-255-4},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Demand Prediction using Machine Learning Methods and Stacked Generalization
SN - 978-989-758-255-4
AU - Tugay, R.
AU - Gündüz Öğüdücü, Ş.
PY - 2017
SP - 216
EP - 222
DO - 10.5220/0006431602160222

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