Fuzzy Rule-Based Intelligent System for Predicting Hotel Occupancy

EMU I-REP

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dc.contributor.advisor Aliyev, Rashad
dc.contributor.author Salehi, Sara
dc.date.accessioned 2021-12-03T13:32:34Z
dc.date.available 2021-12-03T13:32:34Z
dc.date.issued 2020
dc.date.submitted 2020-01
dc.identifier.citation Salehi, Sara. (2020). Fuzzy Rule-Based Intelligent System for Predicting Hotel Occupancy. Thesis (Ph.D.), Eastern Mediterranean University, Institute of Graduate Studies and Research, Dept. of Mathematics, Famagusta: North Cyprus. en_US
dc.identifier.uri http://hdl.handle.net/11129/5249
dc.description Doctor of Philosophy in Applied Mathematics and Computer Science. Thesis (Ph.D.)--Eastern Mediterranean University, Faculty of Arts and Sciences, Dept. of Mathematics, 2020. Supervisor: Prof. Dr. Rashad Aliyev. en_US
dc.description.abstract Accuracy and interpretability have always been significant issues in forecasting methods, and it is very important to have a balance between these issues when developing a system in tourism demand forecasting. There are various clustering algorithms used in many branches. The efficiency of the clustering technique is stipulated by the performance of the clustering results. Fuzzy c-means algorithm is highly efficient for unbiased clustering. In this thesis, fuzzy cmeans algorithm is applied on monthly number of guest arrivals in one of the hotels of North Cyprus over 40 months to find the optimal number of clusters in the analysis problem. Also, the fuzzy rule-based system model for hotel occupancy forecasting is developed, and in order to enhance the comprehensibility and accuracy of this model, Mamdani fuzzy rule-based system is used. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the forecasting model with 7 clusters and 4 inputs provides an optimal solution of the problem. Keywords: Forecasting, Time series, Fuzzy c-means clustering, Fuzzy rule-based system, Mamdani model en_US
dc.description.abstract OZ: Do˘gruluk ve yorumlanabilirlik, ¨ong¨or¨ulme y¨ontemlerinde her zaman ¨onemli konular olmus¸tur ve bu konular arasında dengeyi sa˘glamak, turizm talebinin ¨ong¨or¨ulmesi ic¸in sistemin gelis¸tirilmesinde c¸ok ¨onemlidir. Birc¸ok alanda kullanılan c¸es¸itli k¨umeleme algoritmaları mevcuttur. K¨umeleme tekni˘ginin verimlili˘gi k¨umeleme sonuc¸larının performansı ile belirlenir. Bulanık c-ortalama algoritması tarafsız k¨umeleme ic¸in y¨uksek verimlili˘ge sahiptir. Bu tezde, bulanık c-ortalama algoritması, 40 ay boyunca Kuzey Kıbrıs’taki otellerden birine gelen misafir sayısı ic¸in uygulanır ve bu algoritmanın amacı, analiz probleminde k¨umelerin en uygun sayısını bulmaktır. Ayrıca, bulanık kural tabanlı sistem modeli, otel doluluk oranını tahmin etmek ic¸in gelis¸tirilir ve bu modelin anlas¸ılırlı˘gını ve do˘grulu˘gunu arttırmak ic¸in Mamdani kural tabanlı sistem kullanılır. Tahmin do˘gruluk ¨olc¸ ¨um¨u ic¸in kullanılan ¨olc¸evler olan hataların ortalama kare k¨ok¨u ve ortalama mutlak y¨uzde hatalarının de˘gerlerine dayanarak, 7 k¨umeli ve 4 giris¸li tahmin modeli problemin en uygun c¸ ¨oz¨um¨un¨un sa˘glanmasını tanımlar. Anahtar Kelimeler: Tahmin, Zaman serisi, Bulanık c-ortalama k¨umeleme, Bulanık kural tabanlı sistem, Mamdani modeli en_US
dc.language.iso eng en_US
dc.publisher Eastern Mediterranean University (EMU) - Doğu Akdeniz Üniversitesi (DAÜ) en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Mathematics en_US
dc.subject Applied Mathematics and Computer Science en_US
dc.subject Forecasting en_US
dc.subject Time series en_US
dc.subject Fuzzy c-means clustering en_US
dc.subject Fuzzy rule-based system en_US
dc.subject Mamdani model en_US
dc.title Fuzzy Rule-Based Intelligent System for Predicting Hotel Occupancy en_US
dc.type doctoralThesis en_US
dc.contributor.department Eastern Mediterranean University, Faculty of Arts and Sciences, Dept. of Mathematics en_US


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