| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| MAT5021 | Time Series Analysis | Fall | 3 | 0 | 3 | 12 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | Tr |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Prof. Dr. İRİNİ DİMİTRİYADİS |
| 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. |
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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) |
| 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. |
| 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. | ||
| Course Notes: | 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: |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | % 0 | |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | % 0 | |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | 4 | % 40 |
| Seminar | % 0 | |
| Midterms | 1 | % 30 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 30 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 30 | |
| PERCENTAGE OF FINAL WORK | % 70 | |
| Total | % 100 | |
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Special Course Internship (Work Placement) | 0 | 0 | 0 |
| Field Work | 0 | 0 | 0 |
| Study Hours Out of Class | 0 | 0 | 0 |
| Presentations / Seminar | 0 | 0 | 0 |
| Project | 4 | 29 | 116 |
| Homework Assignments | 0 | 0 | 0 |
| Quizzes | 0 | 0 | 0 |
| Preliminary Jury | 0 | ||
| Midterms | 1 | 17 | 17 |
| Paper Submission | 0 | ||
| Jury | 0 | ||
| Final | 1 | 25 | 25 |
| Total Workload | 200 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |