ECTS - Forecasting
Forecasting (IE519) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Forecasting | IE519 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Ph.D. |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Question and Answer, Problem Solving. |
| Course Lecturer(s) |
|
| Course Objectives | In this course, the students will be learning the role of forecasting in engineering design. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Forecasting methodology and techniques; dynamic Bayesian modelling; methodological forecasting and analysis; polynomial, seasonal, harmonic and regression systems; superpositioning; variance learning; forecast monitoring and applications; time series analysis and forecasting; moving averages; estimation and forecasting for arma models; arma models; |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Forecasting methodology and techniques | |
| 2 | Forecasting methods versus Forecasting Systems; Dynamic Bayesian Modelling; | |
| 3 | Methodological Forecasting and Analysis | |
| 4 | Polynomial, Seasonal, Harmonic and Regression Systems | |
| 5 | Superpositioning | |
| 6 | Variance Learning; Forecast Monitoring and applications; | |
| 7 | Time Series Analysis and Forecasting; Moving Averages | |
| 8 | Estimation and Forecasting for ARMA models; | |
| 9 | ARIMA models | |
| 10 | Seasonal and Non Seasonal Box-Jenkins Models | |
| 11 | Midterm | |
| 12 | Winters’ Exponential Smoothing | |
| 13 | Decomposition Models | |
| 14 | Other possible methods | |
| 15 | Real world applications | |
| 16 | Final Examination Period |
Sources
| Course Book | 1. Makridakis S.G., Wheelright S.C., Hyndman R.J., Forecasting: Methods and Applications, Wiley, 1997. |
|---|---|
| Other Sources | 2. Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, John Wiley and Sons, Inc., 4th Edition, June 2006. |
| 3. Milton, J.S. and Arnold, J.C., Introduction to Probability and Statistics: Principles and Applications for Engineering and the Computing Sciences, McGraw-Hill, 4th edition, 2002. | |
| 4. Ross, S. Introduction to Probability and Statistics for Engineers and Scientists, Academic Press, 3rd edition, 2004. | |
| 5. Triola, M.F., Essentials of Statistics, Addison Wesley,2nd edition, 2004. | |
| 6. Hines, W.W. and Montgomery,D.A., Probability and Statistics in Engineering and Management Science, John Wiley,1990. | |
| 7. Navidi,W. Statistics for Engineers and Scientists, McGraw-Hill, 2008. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | 1 | 30 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 30 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 3 | 100 |
| Percentage of Semester Work | 60 |
|---|---|
| Percentage of Final Work | 40 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Ability to carry out advanced research activities, both individual and as a member of a team | |||||
| 2 | Ability to evaluate research topics and comment with scientific reasoning | |||||
| 3 | Ability to initiate and create new methodologies, implement them on novel research areas and topics | |||||
| 4 | Ability to produce experimental and/or analytical data in systematic manner, discuss and evaluate data to lead scintific conclusions | |||||
| 5 | Ability to apply scientific philosophy on analysis, modelling and design of engineering systems | |||||
| 6 | Ability to synthesis available knowledge on his/her domain to initiate, to carry, complete and present novel research at international level | |||||
| 7 | Contribute scientific and technological advancements on engineering domain of his/her interest area | |||||
| 8 | Contribute industrial and scientific advancements to improve the society through research activities | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 1 | 16 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 4 | 4 |
| Report | |||
| Homework Assignments | 4 | 4 | 16 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 16 | 16 |
| Prepration of Final Exams/Final Jury | 1 | 25 | 25 |
| Total Workload | 125 | ||