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Article type: Research Article
Authors: Ozdemir, Cagataya; * | Onar, Sezi Cevika | Bagriyanik, Selamib | Kahraman, Cengiza | Akalin, Burak Zaferc | Öztayşi, Başara
Affiliations: [a] Industrial Engineering Department, ITU İstanbul, Turkey | [b] Software Engineering Department, Nisantasi University İstanbul, Turkey | [c] Digital Business Services, Turkcell, İstanbul, Turkey
Correspondence: [*] Corresponding author. Cagatay Ozdemir. E-mail: [email protected].
Abstract: Companies started to determine their strategies based on intelligent data analysis due to stagey enhance data production. Literature reviews show that the number of resources where demand estimation, location analysis, and decision-making technique applied together with the machine learning method is low in all sectors and almost none in the shopping mall domain. Within this study’s scope, a new hybrid fuzzy prediction method has been developed that will estimate the customer numbers for shopping malls. This new methodology is applied to predict the number of visitors of three shopping malls on the Anatolian side of Istanbul. The forecasting study for corresponding shopping malls is made by using the daily signaling data from indoor base stations of large-scale technology and telecommunications services provider and the features to be used in machine learning models is determined by fuzzy multi criteria decision making method. Output revealed by the application of the fuzzy multi criteria decision making method enables the prioritization of features.
Keywords: Shopping malls, customer strategy, machine learning, location analysis, hybrid fuzzy prediction method, multi-criteria decision making
DOI: 10.3233/JIFS-219175
Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 63-76, 2022
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