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| IST4012 | Time Series Analysis | 4+0+0 | ECTS:6 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Elective | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Erdinç KARAKULLUKÇU | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | 1. To introduce students to time series methods in detail.
2. To give information to the students at a level that can analyze time series data with the help of SPSS program. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | apply the basic methods used in univariate time series analysis. | 1,2,3,4 | 1, | | LO - 2 : | compare time series analysis methods and obtained results with each other. | 1,2,4 | 1, | | LO - 3 : | apply time series analysis using SPSS program. | 1,2,3,4 | 1, | | LO - 4 : | make predictions for the future using any univariate time series data. | 1,2,3,4 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| 1. Basic concepts
2. Decomposition Method
3. Regression Analysis
4. Exponential Smoothing Method
5. Box-Jenkins Models |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basic concepts and fundemantal procedures | | | Week 2 | Introducing the parts of the SPSS program related to time series analysis | | | Week 3 | Decomposition method | | | Week 4 | Application of decomposition method using SPSS program | | | Week 5 | Regression models for series having trend | | | Week 6 | Application of regression models using SPSS program | | | Week 7 | Additive and multiplicative regression models | | | Week 8 | Application of additive and multiplicative regression models using SPSS program | | | Week 9 | Midterm exam week | | | Week 10 | Exponential Smoothing Methods for series having trend | | | Week 11 | Application of exponential smoothing methods using SPSS program | | | Week 12 | Additive and multiplicative exponential smoothing methods | | | Week 13 | Application of additive and multiplicative exponential smoothing methods using SPSS program | | | Week 14 | Box-Jenkins models | | | Week 15 | Application of Box-Jenkins models | | | Week 16 | Final exam week | | | |
| 1 | Kadılar, C. ve Öncel Çekim, H. 2020; SPSS ve R Uygulamalı Zaman Serileri Analizine Giriş, Seçkin Yayınları, Ankara. | | | |
| 1 | Kadılar, C. 2009; Uygulamalı Zaman Serileri Analizine Giriş, İkinci Baskı, Bizim Büro Basımevi, Ankara.
| | | 2 | Gaynor, P.E., Kirkpatrick, R.C. 1994; Introduction to Time Series Modelling and Forecasting in Business and Economics, Mc.Graw-Hill Inc.
| | | 3 | Wei, W.W.S. 1990; Time Series Analysis, Addison-Wesley Publishing Company.
| | | 4 | Yaffee, R.A. and McGee, M. 2000. Introduction to Time Series Analysis and Forecasting, Academic Press. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1,5 | 50 | | End-of-term exam | 16 | | 1,5 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 4 | 14 | 56 | | Sınıf dışı çalışma | 1 | 11 | 11 | | Laboratuar çalışması | 2 | 5 | 10 | | Arasınav için hazırlık | 2 | 8 | 16 | | Arasınav | 1.5 | 1 | 1.5 | | Dönem sonu sınavı için hazırlık | 3 | 8 | 24 | | Dönem sonu sınavı | 1.5 | 1 | 1.5 | | Total work load | | | 120 |
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