Forecasting (IE519) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Forecasting IE519 3 0 0 3 5
Pre-requisite Course(s)
Course Language English
Course Type N/A
Course Level Ph.D.
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
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;
  • Acquaintance of students with the fundamental concepts of forecasting in engineering projects.
  • Ability of students to develop an insight about the role of forecasting for the industrial world.
  • Ability of students to evaluate and solve real life processes and problems using a forecasting model.
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


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

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Special Course Internship
Field Work
Study Hours Out of Class 16 1 16
Presentation/Seminar Prepration
Project 1 4 4
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