|
|
| OREL7282 | Forecasting Methods in Forest Industry | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of FOREST INDUSTRY ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi İbrahim YILDIRIM | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Forest products production forecasts for the future in order to achieve planned manufacturing industry. To establish multivariate models. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | To understand the qualitative and quantitative forecasting methods. | 1,2,3,8 | 1 | | PO - 2 : | To obtain information about time series. | 1,2,3,8 | 1,4 | | PO - 3 : | To explain trends and seasonal fluctuations. | 1,2,3,8 | 1,4 | | PO - 4 : | To apply the multivariate models. | 1,2,3,8 | 1,4 | | 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), PO : Learning Outcome | | |
| Forest products demand forecasts in the industry, qualitative and quantitative methods, basic properties of time series, the main elements of time series analysis, trends, seasonal fluctuations, extreme value and impressive observations, regression models, delay distributed models, non-linear models, multivariate models. |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Qualitative and quantitative data | | | Week 2 | Qualitative and quantitative forecasting methods | | | Week 3 | Qualitative and quantitative forecasting methods | | | Week 4 | The basic properties of time series | | | Week 5 | Time series analysis | | | Week 6 | Seasonal fluctuations | | | Week 7 | Regression models | | | Week 8 | Regression models | | | Week 9 | Midterm | | | Week 10 | Extreme values and impressive observations | | | Week 11 | Delay distributed models | | | Week 12 | Midterm | | | Week 13 | Linear models | | | Week 14 | Nonlinear models | | | Week 15 | Multivariate models | | | Week 16 | Final Exam | | | |
| 1 | Çekerol, G.S. ve Ulukan, A., 2012. Kantitatif Tahmin Yöntemleri | | | |
| 1 | Göktaş, Ö., 2005. Teorik ve Uygulamalı Zaman Serileri Analizi | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Presentation | 15 | 16.06.2023 | 2 | 20 | | Homework/Assignment/Term-paper | 15 | 16.06.2023 | 2 | 30 | | End-of-term exam | 16 | 23.06.2023 | 2 | 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 | 6 | 14 | 84 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 6 | 4 | 24 | | Arasınav | 2 | 1 | 2 | | Uygulama | 0 | 0 | 0 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 0 | 0 | 0 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 7 | 5 | 35 | | Dönem sonu sınavı | 2 | 1 | 2 | | Diğer 1 | 6 | 5 | 30 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 219 |
|