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Comparing Soft Computing Techniques for Estimating Demand of Season Ticket Holders

by Mehmet Şahin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 20
Year of Publication: 2018
Authors: Mehmet Şahin
10.5120/ijca2018917989

Mehmet Şahin . Comparing Soft Computing Techniques for Estimating Demand of Season Ticket Holders. International Journal of Computer Applications. 182, 20 ( Oct 2018), 34-38. DOI=10.5120/ijca2018917989

@article{ 10.5120/ijca2018917989,
author = { Mehmet Şahin },
title = { Comparing Soft Computing Techniques for Estimating Demand of Season Ticket Holders },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2018 },
volume = { 182 },
number = { 20 },
month = { Oct },
year = { 2018 },
issn = { 0975-8887 },
pages = { 34-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number20/30052-2018917989/ },
doi = { 10.5120/ijca2018917989 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:11:59.259656+05:30
%A Mehmet Şahin
%T Comparing Soft Computing Techniques for Estimating Demand of Season Ticket Holders
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 20
%P 34-38
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sports have macroeconomic effects and profound impacts on the level of development of countries. Therefore, sports economics deserves and receives attention in the literature. In this study, season ticket holders (STHs), who have a significant effect on sports economics, are the main concern. STHs have strategic and financial importance to professional sports teams. STHs play a key role on stadium economics of soccer teams. Considering these points, soft computing techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN), were utilized to predict demand of season ticket holders of soccer clubs in this study. Real data of soccer clubs from multiple countries were used in the training and testing processes of the models. The performance measures indicated that both of the proposed models could be used for prediction purposes effectively. However, the ANFIS model with hybrid optimization outperformed the ANN model. Contrary to the studies concentrating on analyzing determinants of the STH demand, this study is the first for predicting the demand of season ticket holders by using two competitive methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Stadium Economics ANFIS Demand Forecasting ANN

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