loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Resul Tugay and Şule Gündüz Öğüdücü

Affiliation: Istanbul Technical University, Turkey

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 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.221.183.34

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - DATA; ISBN 978-989-758-255-4; ISSN 2184-285X, SciTePress, 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 - DATA},
year={2017},
pages={216-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006431602160222},
isbn={978-989-758-255-4},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Data Science, Technology and Applications - DATA
TI - Demand Prediction using Machine Learning Methods and Stacked Generalization
SN - 978-989-758-255-4
IS - 2184-285X
AU - Tugay, R.
AU - Gündüz Öğüdücü, Ş.
PY - 2017
SP - 216
EP - 222
DO - 10.5220/0006431602160222
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic