Information Package / Course Catalogue
Time Series Analysis
Course Code: BİS527
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 4
Objectives of the Course

In this course you will develop a sound understanding of the time domain properties and common models for stationary and non-stationary time series in discrete time and will be able to use SPSS package to perform appropriate analyses.

Course Content

Time series methods, the periodogram, basic theory of stationary processes, linear filters, spectral analysis, ARIMA models, forecasting, smoothing, autoregression and time series regression models.

Name of Lecturer(s)
Learning Outcomes
1.To be able to familiar with properties of the major types of time series observed in discrete time
2.To be able to identify appropriate models for such series
3.To be able to estimate these models using SPSS software package.
4. To be able to comprehend how to diagnose model adequacy
5.To be able to comprehend linear prediction for a range of time series models
6. To be able to make substantive analysis of several time series and write a major report presenting one of these analyses
Recommended or Required Reading
Weekly Detailed Course Contents
Week 1 - Theoretical
Time series definition and general features
Week 1 - Practice
Application with package programs
Week 2 - Theoretical
Time series analysis and its stages
Week 2 - Practice
Application with package programs
Week 3 - Theoretical
Separation of time series into its components
Week 3 - Practice
Application with package programs
Week 4 - Theoretical
Non-stationary time series
Week 4 - Practice
Application with package programs
Week 5 - Theoretical
Stationary time series
Week 5 - Practice
Application with package programs
Week 6 - Theoretical
Testing stationarity, unit root test
Week 6 - Practice
Application with package programs
Week 7 - Theoretical
Stationarizing techniques in time series
Week 7 - Practice
Application with package programs
Week 8 - Theoretical
Literature review and discussion (Midterm exam)
Week 9 - Theoretical
Autoregressive models
Week 9 - Practice
Application with package programs
Week 10 - Theoretical
Moving average models
Week 10 - Practice
Application with package programs
Week 11 - Theoretical
Autoregressive moving average models-I
Week 11 - Practice
Application with package programs
Week 12 - Theoretical
Autoregressive moving average models-II
Week 12 - Practice
Application with package programs
Week 13 - Theoretical
Autoregressive integrated moving average models-I
Week 13 - Practice
Application with package programs
Week 14 - Theoretical
Autoregressive integrated moving average models-II
Week 14 - Practice
Application with package programs
Week 15 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Attending Lectures1%5
Assignment1%5
Midterm Examination1%20
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140228
Assignment1505
Quiz2216
Midterm Examination110212
Final Examination115217
TOTAL WORKLOAD (hours)96
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
OÇ-1
3
4
4
4
4
3
3
4
4
2
OÇ-2
3
5
4
3
5
4
4
4
5
4
OÇ-3
1
5
4
4
5
4
5
5
5
5
OÇ-4
3
4
4
3
4
4
4
3
4
4
OÇ-5
3
3
2
3
4
3
3
4
4
4
OÇ-6
3
4
4
3
5
4
4
5
3
5
Adnan Menderes University - Information Package / Course Catalogue
2026