Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
İST354 Time Series Analysis 927006 3 6 6
Level of Course Unit
First Cycle
Objectives of the Course
To understand the basic features of a time series and to introduce the methods commonly used in forecasting of univariate time series and their uses in practice.
Name of Lecturer(s)
Prof.Dr.V.Rezan Uslu
Learning Outcomes
  1. He/she can interpret of time sequence plot of a time series and discriminate their components.
  2. He/she can analyze the real world time series data using with statistical packages.
  3. He/she can apply the time series methods very professionally to univariate data.
Mode of Delivery
Formal Education
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Recommended or Required Reading
Mustafa Sevüktekin ve Mehmet Çınar – Ekonometrik Zaman Serileri AnaliziDamodar Gujarati-Temel EkonometriYılmaz Akdi-Zaman Serileri Analizi
Planned Learning Activities and Teaching Methods
Language of Instruction
Turkish
Work Placement(s)
None
Course Contents
The basic concepts and definitions of time series, their components and the methods for univariate time series analysis.
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.Fundamental concepts, definitions, components of a time series, time series plot
2.Moving averages techniques and their applications in MINITAB and SPSS
3.Exponential smoothing techniques and their applications in MINITAB and SPSS
4.The concept of the lagged variable, difference operator and their applications in MINITAB and SPSS
5.The definition of stationarity, and stationary time series
6.Non-stationary time series
7.Autocorrelation and partial autocorrelation function of a time series
8.Midterm
9.Introduction to the methodology of Box-Jenkins
10.Auto-regressive time series models (AR(p))
11.Moving averages time series models (MA(q))
12.Auto-regressive and Moving averages time series models (ARMA(p,q))
13.The measures of goodness of fit
14.General review and exercies
15.
16.
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination1100
SUM100
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
SUM100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
SUM100
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination122
Final Examination133
Attending Lectures13339
Practice5315
Problem Solving10110
Question-Answer515
Self Study13339
Individual Study for Mid term Examination5315
Individual Study for Final Examination5315
Homework236
SUM149