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	Natural & Applied Sciences Master's Degree
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

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
#	Program Qualifications / Competencies	1	2	3	4	5
1	Has the ability to apply scientific knowledge gained in the undergraduate education and to expand and extend knowledge in the same or in a different area.				X
2	Has the ability to obtain, to evaluate, to interpret and to apply information by doing scientific research.			X
3	Can apply gained knowledge and problem solving abilities in inter-disciplinary research.					X
4	Has the ability to work independently within research area, to state the problem, to develop solution techniques, to solve the problem, to evaluate the obtained results and to apply them when necessary.				X
5	Takes responsibility individually and as a team member to improve systematic approaches to produce solutions in unexpected complicated situations related to the area of study.			X
6	Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework.				X
7	Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility.			X
8	Has the ability to follow recent developments within the area of research, to support research with scientific arguments and data, to communicate the information on the area of expertise in a systematically by means of written report and oral/visual presentation.				X
9	Has proficiency in English language and has the ability to communicate with colleagues and to follow the innovations in mathematics and related fields.			X
10	Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge.	X
11	Has professional ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications.

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