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: 6
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 - Intermediate Exam
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 - Theoretical
Literature review and discussion
Week 15 - Practice
Literature review and discussion
Week 16 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140228
Assignment110010
Quiz142142
Midterm Examination120222
Final Examination120222
TOTAL WORKLOAD (hours)152
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