APPLIED MATHEMATICS (TURKISH, NON-THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
MAT5021 Time Series Analysis Fall 3 0 3 12
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: Turkish
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Prof. Dr. İRİNİ DİMİTRİYADİS
Recommended Optional Program Components: None
Course Objectives: The objective is to let the student understand the time dependent change in a random variable, to grasp the differences between the different methods and learn how to apply time series analysis to autoregresive data sets.

Learning Outcomes

The students who have succeeded in this course;
The student will be able to distinguish data sets that may be termed as time series data, will be able to distinguish autregresive data from others, will be able to fit autoregresive data to alternative models, will be able to choose the best fitting model and carry meaningful predictions. The student will be able to use relevant computer programs (eg.e-views)


Course Content

Introduction to time series, definition and properties of alternative time series models, estimation of coefficients of time series,stationarity tests,choosing the best fitting model, using models for predicition and interpretation of data.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Introduction to time series. Time series data, basic modeling, principles of stochastic modeling.
2) Defining components of time series.
3) Definition and properties of Autoregresive (AR) time series.
4) Definition and properties of Moving Average (MA)time series.
5) Definition and properties of autoregesive moving average time series.
6) Definition and properties of non-stationary autoregresive moving average (ARIMA) time series.
7) Tests of stationarity.
8) Problem solutions.
9) The Box-Jenkins method and properties. Estimation with the Box Jenkins method.
10) Box Jenkins method continued.
11) Definition and properties of GARCH model.
12) Definition and properties of the ARCH-M model.
13) The vector autoregresive model.
14) Cointigration technique.

Sources

Course Notes / Textbooks: Turkish books:
1. Ekonometrik Zaman Serileri Analizi EViews Uygulamalı, M. Sevüktekin ve M. Nargeleçekenler, Nobel Yayın, 2007. 2. Zaman Serileri Analizi, H. Bozkurt, Ekin Kitabevi, 2007. YARDIMCI KİTAPLAR: 3. Zaman Serileri Analizi (Birim Kökler ve Kointegrasyon), Y. Akdi, Bıçaklar Kitabevi, 2003.

English references

Time Series Analysis and Its Applications With R Examples, R.H. Shumway and D.S. Stoffer, Springer, 2006.

Statistical Methods for Forecasting, B. Abraham and J. Ledolter, John Wiley and Sons, Inc. Publication, 2005.
References:

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Project 4 % 40
Midterms 1 % 30
Final 1 % 30
Total % 100
PERCENTAGE OF SEMESTER WORK % 30
PERCENTAGE OF FINAL WORK % 70
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Project 4 29 116
Midterms 1 17 17
Final 1 25 25
Total Workload 200

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) Ability to assimilate mathematic related concepts and associate these concepts with each other.
2) Ability to gain qualifications based on basic mathematical skills, problem solving, reasoning, association and generalization.
3) Be able to organize events, for the development of critical and creative thinking and problem solving skills, by using appropriate methods and techniques.
4) Ability to make individual and team work on issues related to working and social life.
5) Ability to transfer ideas and suggestions, related to topics about his/her field of interest, written and verball.
6) Ability to use mathematical knowledge in technology.
7) To apply mathematical principles to real world problems.
8) Ability to use the approaches and knowledge of other disciplines in Mathematics.
9) Be able to set up and develope a solution method for a problem in mathematics independently, be able to solve and evaluate the results and to apply them if necessary.
10) To apply mathematical principles to real world problems.
11) To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively.
12) To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself.