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IST4012Time Series Analysis4+0+0ECTS:6
Year / SemesterSpring Semester
Level of CourseFirst Cycle
Status Elective
DepartmentDEPARTMENT of STATISTICS and COMPUTER SCIENCES
Prerequisites and co-requisitesNone
Mode of Delivery
Contact Hours14 weeks - 4 hours of lectures per week
LecturerDr. Öğr. Üyesi Erdinç KARAKULLUKÇU
Co-Lecturer
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
1. To introduce students to time series methods in detail. 2. To give information to the students at a level that can analyze time series data with the help of SPSS program.
 
Learning OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
LO - 1 : apply the basic methods used in univariate time series analysis.1,2,31,
LO - 2 : compare time series analysis methods and obtained results with each other.1,2,31,
LO - 3 : apply time series analysis using SPSS program.1,2,31,
LO - 4 : make predictions for the future using any univariate time series data.1,2,31,
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome

 
Contents of the Course
1. Basic concepts 2. Decomposition Method 3. Regression Analysis 4. Exponential Smoothing Method 5. Box-Jenkins Models
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Basic concepts and fundemantal procedures
 Week 2Introducing the parts of the SPSS program related to time series analysis
 Week 3Decomposition method
 Week 4Application of decomposition method using SPSS program
 Week 5Regression models for series having trend
 Week 6Application of regression models using SPSS program
 Week 7Additive and multiplicative regression models
 Week 8Application of additive and multiplicative regression models using SPSS program
 Week 9Midterm exam week
 Week 10Exponential Smoothing Methods for series having trend
 Week 11Application of exponential smoothing methods using SPSS program
 Week 12Additive and multiplicative exponential smoothing methods
 Week 13Application of additive and multiplicative exponential smoothing methods using SPSS program
 Week 14Box-Jenkins models
 Week 15Application of Box-Jenkins models
 Week 16Final exam week
 
Textbook / Material
1Kadılar, C. ve Öncel Çekim, H. 2020; SPSS ve R Uygulamalı Zaman Serileri Analizine Giriş, Seçkin Yayınları, Ankara.
 
Recommended Reading
1Kadılar, C. 2009; Uygulamalı Zaman Serileri Analizine Giriş, İkinci Baskı, Bizim Büro Basımevi, Ankara.
2Gaynor, P.E., Kirkpatrick, R.C. 1994; Introduction to Time Series Modelling and Forecasting in Business and Economics, Mc.Graw-Hill Inc.
3Wei, W.W.S. 1990; Time Series Analysis, Addison-Wesley Publishing Company.
4Yaffee, R.A. and McGee, M. 2000. Introduction to Time Series Analysis and Forecasting, Academic Press.
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 1,5 50
End-of-term exam 16 1,5 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 4 14 56
Sınıf dışı çalışma 1 5 5
Laboratuar çalışması 2 5 10
Arasınav için hazırlık 5 1 5
Arasınav 1.5 1 1.5
Dönem sonu sınavı için hazırlık 5 1 5
Dönem sonu sınavı 1.5 1 1.5
Total work load84