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Course Information
Course Unit Title : Modeling and Forecasting in Tourism Business
Course Unit Code : 02TIS5132
Type of Course Unit : Optional
Level of Course Unit : Second Cycle
Year of Study : Preb
Semester : 255.Semester
Number of ECTS Credits Allocated : 6,00
Name of Lecturer(s) : ---
Course Assistants :
Learning Outcomes of The Course Unit : Knows modeling and forecasting techniques in tourism business and applies them.
Understand the importance of demand forecasts and modeling in tourism sector
Knows and applies qualititative methods of estimation.
Knows Quantitative (numerical) and applies forecasting techniques.
Knows and applies time series and economtric forecasting methods in tourism business.
Knows alternative demand forecasting methods in tourism business.
Mode of Delivery : Face-To-Face
Prerequisities and Co-requisities Courses : Unavailable
Recommended Optional Programme Components : Unavailable
Course Contents : The importance of tourism demand forecasting and modeling.
Modeling and Forecasting Techniques
Qualitative Estimation Methods
Quantitative (numerical) estimation techniques
Time series methods
Causal (regression-based) estimation techniques
Demand forecasting practices in the tourism sector
Tourism demand estimates, methods of artificial intelligence
Languages of Instruction : Turkish
Course Goals : Demand Forecasting Methods to gain competence in the use of enterprises operating in the tourism industry.
Course Aims : Tourism development based on scientific basis, estimating methods, a feature that makes it easier to make decisions of those in the case of the manager. In this way, the economy, and investors will be able to prepare a flexible development plans on issues such as the prevention of waste of resources to be allocated the necessary precautions can be taken in time. This course aims to gain competence on modeling and forecasting for students the needs of the tourism sector
WorkPlacement   Work placement is not necessary.
Recommended or Required Reading
Textbook : 1- ÇUHADAR Murat, ÇÖĞÜRCÜ İclal, KÜKRER Ceyda (2014). Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures. International Journal of Business and Social Research (IJBSR), Volume -4, No.-3, March, 2014, 12-28.
2- ÇUHADAR Murat (2014). Modelling and Forecasting Inbound Tourism Demand to Istanbul: A Comparative Analysis, European Journal of Business and Social Sciences (EJBSS), Vol.2 No.12, March 2014, 101-119.
3-ÇUHADAR Murat (2014). "Building Proper Forecast Model for Daily Air Passenger Demand: A Study of Antalya International Airport", International Antalya Hospitality Tourism And Travel Research, Washington State University & Akdeniz University, Porto Bello Hotel Antalya Turkey, December 9-12, 2014(
Additional Resources : 1- Tourism Demand Modelling and Forecasting: Modern Econometric Approaches, Haiyan Song, Witt S F. Elsevier. 2000.
2- Advanced Econometrics of Tourism Demand, The. Routledge Advances in Tourism, Volume 13. 2010. Haiyan Song, Stephen F. Witt, Gang Li.
3- Tourism Supply Chain Management, Haiyan Song, 2013. Routledge.
Material Sharing
Documents :
Assignments :
Exams :
Additional Material :
Planned Learning Activities and Teaching Methods
Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
ECTS / Table Of Workload (Number of ECTS credits allocated)
Student workload surveys utilized to determine ECTS credits.
Activity :
Number Duration Total  
Course Duration (Excluding Exam Week) :
12 3 36  
Time Of Studying Out Of Class :
7 2 14  
Homeworks :
1 10 10  
Presentation :
1 10 10  
Project :
0 0 0  
Lab Study :
0 0 0  
Field Study :
0 0 0  
Visas :
1 10 10  
Finals :
1 20 20  
Workload Hour (30) :
30  
Total Work Charge / Hour :
0  
Course's ECTS Credit :
0      
Assessment Methods and Criteria
Studies During Halfterm :
Number Co-Effient
Visa :
1 30
Quiz :
0 0
Homework :
1 30
Attendance :
1 30
Application :
1 10
Lab :
0 0
Project :
0 0
Workshop :
0 0
Seminary :
0 0
Field study :
0 0
   
TOTAL :
100
The ratio of the term to success :
40
The ratio of final to success :
60
TOTAL :
100
Weekly Detailed Course Content
Week Topics  
1 Businesses in the tourism sector and their general characteristics.
 
2 The importance of demand forecasting in planning process of tourism enterprises.
 
3 Qualitative forecasting methods and their practices of tourism enterprises: Scenario Writing and Delphi Methods.
 
4 Quantitative (quantitative) estimation techniques, and practices in the tourism industry.
 
5 The use of univariate (Time Series)techniques in Tourism demand modeling and forecasting .
 
6 Exponential Smoothing methods: Modeling and forecasting Tourism demand by Holt and Winter's smoothing methods.
 
7 Mid Term Exam.
 
8 Modeling and forecasting with Box-Jenkins Methodology: modeling and forecasting tourism demand by ARIMA methods. Application examples from tourism sector.
 
9 Causal (regression-based) Forecasting Techniques and applications in tourism enterprises.
 
10 Artificial Intelligence Methods: Concepts, principles and the use in tourism demand forecasting.
 
11 Tourism demand forecasting with artificial neural networks: The advantages and disadvantages of the different architecture models
 
12 Genetic algorithms and fuzzy logic techniques: The use of Tourism demand forecasting and application examples.
 
13 The use of hybrid methods in Tourism demand forecasting: Application examples of Adaptive Neuro-Fuzzy Inference Systems (ANFIS).
 
14 Performance evaluation of alternative modeling and forecasting methods.
 
0 Final Exam
 
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