Industrial Engineering

Course Details

KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details

Course Code	Course Name	Year	Period	Semester	T+A+L	Credit	ECTS
15281834	Time Series and Forecasting Techniques	4	Spring	8	3+0+0	0	5

Course Type	Elective
Course Cycle	Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6)
Course Language	Turkish
Methods and Techniques	-
Mode of Delivery	Face to Face
Prerequisites	-
Coordinator	Prof. Murat DARÇIN
Instructor(s)	-
Instructor Assistant(s)	-

Course Content

Zaman Serilerinin Bileşkelerine Ayrılması Yöntemi.Doğrusal Trend Fonksiyonu. Düzgünleştirme Yöntemleri; Basit Hareketli Ortalamalar,Üstel Düzgünleştirme Yöntemleri,Tekli Üstel Düzgünleştirme, Otoregressif Modeller ve Hareketli Ortalama Yöntemleri.Mevsimlik Otoregressif Hareketli Ortalama Yöntemleri.Fiyat İndeksleri ve Zaman Serileri Analizindeki Önemi, İndeksler,Fiyat İndekslerinde Temel Devre ve Dönüşümü.

Objectives of the Course

Given the basic information needed to make effective predictions.

Contribution of the Course to Field Teaching

Basic Vocational Courses
Specialization / Field Courses	X
Support Courses
Transferable Skills Courses
Humanities, Communication and Management Skills Courses

Relationships between Course Learning Outcomes and Program Outcomes

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Program Learning Outcomes	Level
P1	Knowledge of mathematics, natural sciences, fundamental engineering, computational sciences, and industrial engineering-specific subjects; the ability to apply this knowledge to solve complex industrial engineering problems.	5
P2	The ability to define, formulate, and analyze complex industrial engineering problems using fundamental science, mathematics, and engineering knowledge, while keeping in mind the relevant UN Sustainable Development Goals.	5
P4	The ability to select and utilize appropriate techniques, resources, and modern engineering and information tools, including estimation and modeling, for the analysis and solution of complex industrial engineering problems, while being aware of their limitations.	5

Course Learning Outcomes

Upon the successful completion of this course, students will be able to:
No	Learning Outcomes	Outcome Relationship	Measurement Method **
O1	Zaman serileri analizindeki temel kavramları öğrenir.	P.1.39	1
O2	Mevsim, trend ve rassallık indekslerinin matematiksel altyapısını öğrenir.	P.1.40	1
O3	Çeşitli olaylar karşısında etkili tahminde bulunma becerisi kazanır.	P.1.41	1
O4	Zaman serilerinin uygulama alanlarını öğrenir.	P.1.42	1
O5	Zaman serileri analizindeki temel kavramları öğrenir.	P.2.65	1
O6	Mevsim, trend ve rassallık indekslerinin matematiksel altyapısını öğrenir.	P.2.66	1
O7	Çeşitli olaylar karşısında etkili tahminde bulunma becerisi kazanır.	P.4.29	1
O8	Zaman serilerinin uygulama alanlarını öğrenir.	P.4.30	1
** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7

Weekly Detailed Course Contents

Week	Topics
1	Fundamental concept and introduction to time series, The component of time series, Moving Average Mean, Least Square Method, Linear Trend Function
2	Quadratic Trend Function, Exponential Trend Function, Determining the Trend Function, Determining the seasonality
3	Seasonality Index, Using Seasonality Index, Determining effects of Cyclical Movements and Random Factors
4	Smoothing Methods: Single Moving Averages, Exponential Smoothing Methods
5	Simple Exponential Smoothing, Linear Moving Average, Linear Exponential Smoothing Methods
6	Nonlinear Exponential Smoothing, Other Exponential Smoothing Methods, Autoregressive Models and Method of Moving Average
7	Autocorrelation Coefficients, Calculating Autocorrelation Coefficients and Distribution, Autocorrelation analysis
8	Midterm
9	Autocorrelation Tests, Partial Autocorrelation Coefficients and Tests of Autocorrelation Coefficients, Autoregressive Models, Simple and Multiple Regression Analysis
10	Using Autoregressive Models
11	Moving Average Methods, Moving Average Models, Prediction of Parameters in Moving Average Models
12	Box-Jenkins Method
13	Researching feasibility of the Model, Using model for predictions, Seasonal Autoregressive Moving Average Methods
14	Prices Indexes and its Importance in Time Series Analysis
15	Final Sınavına Hazırlık

Textbook or Material

Resources	Modern Zaman Serileri ve Yöntemleri, NOBEL AKADEMİK YAYINCILIK, Dr. Üyesi Ebrucan İslamoğlu
	Modern Zaman Serileri ve Yöntemleri, NOBEL AKADEMİK YAYINCILIK, Dr. Üyesi Ebrucan İslamoğlu

Evaluation Method and Passing Criteria

In-Term Studies	Quantity	Percentage
Attendance	-	-
Laboratory	-	-
Practice	-	-
Field Study	-	-
Course Specific Internship (If Any)	-	-
Homework	-	-
Presentation	-	-
Projects	-	-
Seminar	-	-
Quiz	-	-
Listening	-	-
Midterms	1	40 (%)
Final Exam	1	60 (%)
Total		100 (%)

ECTS / Working Load Table

	Quantity	Duration	Total Work Load
Course Week Number and Time	14	3	42
Out-of-Class Study Time (Pre-study, Library, Reinforcement)	14	3	42
Midterms	1	30	30
Quiz	0	0	0
Homework	0	0	0
Practice	0	0	0
Laboratory	0	0	0
Project	0	0	0
Workshop	0	0	0
Presentation/Seminar Preparation	0	0	0
Fieldwork	0	0	0
Final Exam	1	36	36
Other	0	0	0
Total Work Load:			150
Total Work Load / 30			5
Course ECTS Credits:			5

Course - Learning Outcomes Matrix

Relationship Levels
Lowest	Low	Medium	High	Highest
1	2	3	4	5

#	Learning Outcomes	P1	P2	P4
O1	Zaman serileri analizindeki temel kavramları öğrenir.	5	-	-
O2	Mevsim, trend ve rassallık indekslerinin matematiksel altyapısını öğrenir.	5	-	-
O3	Çeşitli olaylar karşısında etkili tahminde bulunma becerisi kazanır.	5	-	-
O4	Zaman serilerinin uygulama alanlarını öğrenir.	5	-	-
O5	Zaman serileri analizindeki temel kavramları öğrenir.	-	5	-
O6	Mevsim, trend ve rassallık indekslerinin matematiksel altyapısını öğrenir.	-	5	-
O7	Çeşitli olaylar karşısında etkili tahminde bulunma becerisi kazanır.	-	-	5
O8	Zaman serilerinin uygulama alanlarını öğrenir.	-	-	5