Forecasting Techniques

Course Code:

ISE 457

Course Type:

Area Elective

Credits:

3

Theoric:

3

Practice:

0

Laboratory Hour:

0

ECTS:

5

Prerequisite Courses:

Statistical Applications for Industrial Engineers

Course Language:

English

Course Objectives:

To introduce the basic principles and methods of forecasting. Determining the future state of a process under uncertainty by using statistical principles.

Course Content:

Naive methods, moving average methods, exponential smoothing (simple, Holt’s, Winters’), classical time series decomposition, regression methods, Box-Jenkins ARIMA models, statistical package applications.

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Laboratory 4: Case Study

Course Evaluation Methods:

A: Exam, B: Experiment, C: Homework, D: Project

Vertical Tabs

Ders Tanimlari

Course Learning Outcomes

Course Learning Outcomes	Detailed Program Outcomes	Teaching Methods	Assessment Methods
Ability to collect data, analyze data, interpret and present the results.	5b	1,3	A, D
Ability to forecast the future state of a process under uncertainty by using statistical principles.	1a, 1b	1,3	A, D
Ability to use classical forecasting techniques.	1a, 1b	1,3	A, D
Ability to use regression methods for forecasting.	1a, 1b	1,3	A, D
Ability to use ARIMA models for forecasting.	1a, 1b	1,3	A, D
Can use statistical package MINITAB.	4b	3	A, D
Ability to work in teams on a real life forecasting project.	6a, 7c, 7d	4	D
Being aware of ethical responsibility.	9a		A, D

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction to Forecasting. Naive Methods.	Textbook
2	Moving Averages.	Textbook
3	Exponential Smoothing (Simple, Holt’s and Winters’).	Textbook
4	Time Series Decomposition.	Textbook
5	Computer Applications.	Textbook
6	Regression Methods. Simple Regression. Linear Trends.	Textbook
7	Regression Methods. Multiple Regression. Quadratic Trends.	Textbook
8	MIDTERM EXAM Regression Methods. Dummy Variables. Seasonality.	Textbook
9	Time Series Analysis. MA (moving average) and AR (autoregressive) Models.	Textbook
10	ARMA Models.	Textbook
11	ARIMA Models.	Textbook
12	ARIMA Forecasting.	Textbook
13	Project Presentations
14	Project Presentations

Recommended Sources

RECOMMENDED SOURCES
Textbook	Business Forecasting. J. E. Hanke and D. W. Wichern. Pearson Education.
Additional Resources	Probability and Statistics for Engineers and Scientists, R. E. Walpole, R. H. Myers, S. L. Myers and K. Ye , Pearson Education 2012.

Material Sharing

MATERIAL SHARING
Documents
Assignments	Team Project
Exams	Midterm Exam, Final Exam, 2 Quizzes

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-term	1	30
Project	1	20
Quizzes	2	10
Attendance		5
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		35
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		65
Total		100

Course’s Contribution to Program

COURSE'S CONTRIBUTION TO PROGRAM OUTCOMES
No	Program Learning Outcomes	check √
1a	Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline,	√
1b	Ability to use theoretical and applied knowledge in these areas in complex engineering problems.	√
2a	Ability to identify, formulate, and solve complex engineering problems,
2b	Ability to select and apply proper analysis and modeling methods for this purpose.
3a	Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result,
3b	Ability to apply modern design methods for this purpose.
4a	Ability to devise, select and use modern techniques and tools needed for analyzing and solving complex problems encountered in engineering practice.
4b	Ability to employ information technologies effectively.	√
5a	Ability to design experiments for investigating complex engineering problems or discipline specific research questions,
5b	Ability to conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or discipline specific research questions.	√
6a	Ability to work efficiently in intra-disciplinary teams,	√
6b	Ability to work efficiently in multi-disciplinary teams,
6c	Ability to work individually.
7a	Ability to communicate effectively in Turkish, both orally and in writing,
7b	Knowledge of a minimum of one foreign language,
7c	Ability to write effective reports and comprehend written reports, prepare design and production reports,	√
7d	Ability to make effective presentations,	√
7e	Ability to give and receive clear and intelligible instructions.
8a	Recognition of the need for lifelong learning, ability to access information, ability to follow developments in science and technology,
8b	Ability to continue to educate him/herself.
9a	Consciousness to behave according to ethical principles and professional and ethical responsibility.	√
9b	Knowledge on standards used in engineering practice.
10a	Knowledge about business life practices such as project management, risk management, change management.
10b	Awareness in entrepreneurship and innovation.
10c	Knowledge about sustainable development.
11a	Knowledge about the global and social effects of engineering practices on health, environment, and safety,
11b	Knowledge about contemporary issues of the century reflected into the field of engineering.
11c	Awareness of the legal consequences of engineering solutions.

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Excluding the exam weeks: 13 x Total course hours)	13	3	39
Hours for off-the-classroom study (Pre-study, practice)	14	5	70
Midterm examination	1	2	2
Project	1	10	10
Final examination	1	2	2

Total Work Load			123
Total Work Load / 25 (h)			4.92
ECTS Credit of the Course			5

Search form

Forecasting Techniques

Vertical Tabs

Ders Tanimlari