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END3020 | Forecast Techniques | 3+0+0 | ECTS:5 | Year / Semester | Spring Semester | Level of Course | First Cycle | Status | Elective | Department | DEPARTMENT of INDUSTRIAL ENGINEERING | Prerequisites and co-requisites | None | Mode of Delivery | Face to face | Contact Hours | 14 weeks - 3 hours of lectures per week | Lecturer | Doç. Dr. Hüseyin Avni ES | Co-Lecturer | | Language of instruction | Turkish | Professional practise ( internship ) | None | | The aim of the course: | To be informed about the forecast, which is a sub-element of the decision-making about the future, to ensure the application of the correct forecasting technique for the situation encountered,To be able to use the necessary statistics and computer aided programs and to interpret the results correctly. |
Learning Outcomes | CTPO | TOA | Upon successful completion of the course, the students will be able to : | | | LO - 1 : | Determine the appropriate forecast method according to the problem. Selects the appropriate model for the specified forecast methods | 2 | 1,3 | LO - 2 : | Measures and evaluates the error for estimates, Forecast the future | 11 | 1,3 | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | |
Presentation of forecasting techniques and operation of forecasting system, Evaluation of qualitative and quantitative forecasting methods, Performing computer-aided applications of statistical and artificial intelligence-based forecasting techniques and interpretation of results, Presentation of a real forecasting application |
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Course Syllabus | Week | Subject | Related Notes / Files | Week 1 | Forecast-Plan Concepts,Forecasting Areas, Forecast Types | | Week 2 | Characteristics of Forecasting, Qualitative and Quantitative Forecasting Techniques, Operation of Forecasting System | | Week 3 | Recalling Basic Statistical Concepts (Correlation, Standard Deviation, Hypothesis Testing, Student-t and Normal Distribution etc.) | | Week 4 | Investigation of Data Structure | | Week 5 | Selection of Appropriate Forecasting Techniques,, Experimental Evaluation of Forecasting Techniques, Measurement of Forecast Error | | Week 6 | Naive Models, Meaning Estimation Methods (Simple Averages, Moving Averages, Weighted Moving Averages, Double Moving Averages) | | Week 7 | Exponential Correction Methods (Holt and Winter) | | Week 8 | Minitab/SPSS Application | | Week 9 | Midterm Exam | | Week 10 | Simple Linear Regression, Variance Decomposition, Factor of Analysis, Residual Analysis, Variable Transformations | | Week 11 | Multiple Regression Analysis, Regression Significance, Dummy Variables, Multicollinearity, Selecting the Regression Equation | | Week 12 | Introduction to ARIMA methodology for stationary series | | Week 13 | Minitab/SPSS Application | | Week 14 | Forecasting with artificial intelligence methods | | Week 15 | Project and Homework Presentations | | Week 16 | Final exam | | |
1 | Çekerol, G.S., Ulukan, A. (2012). Kantitatif Tahmin Yöntemleri, Nisan Kitabevi. | | 2 | Hanke, J.E. and D.W. Wichern (2008). Business Forecasting. 8thEdition, Pearson Education International; Harlow, Essex. | | |
1 | Makridakis, S, S.C. Wheelwright, and R.J. Hyndman (1988). Forecasting: Methods and Applications, Third Edition. John Wiley and Sons; New York. | | |
Method of Assessment | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | Mid-term exam | 9 | 11/04/2019 | 1,5 | 30 | Project | 14 | 27/05/2019 | 6 | 20 | End-of-term exam | 16 | 27/05/2019 | 1,5 | 50 | |
Student Work Load and its Distribution | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | Yüz yüze eğitim | 3 | 14 | 42 | Sınıf dışı çalışma | 3 | 14 | 42 | Arasınav için hazırlık | 2.5 | 8 | 20 | Arasınav | 2 | 1 | 2 | Proje | 3 | 10 | 30 | Dönem sonu sınavı için hazırlık | 2 | 6 | 12 | Dönem sonu sınavı | 2 | 1 | 2 | Total work load | | | 150 |
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