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| JDZL7290 | Time Series Analysis in Geodesy | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Emine TANIR KAYIKÇI | | Co-Lecturer | None | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | Understand main structures of time series and doing some analysis for time series decomposition (trend and seasonal effects) |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Learn general structure of time series | | | | PO - 2 : | Model trend and seasonal effects in time series | | | | PO - 3 : | Use time series forecasting | | | | 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), PO : Learning Outcome | | |
| INtroduction to time series, time series data, modelling aims of time series, general features of time series in geodesy, components of time series, time series decomposition, frequency domain analysis, stochastic processes, time series analysis and and forecasting by stochastic models.
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | introduction to time series | | | Week 2 | Understanding the characteristics of time series data | | | Week 3 | Modelling purpose of time series | | | Week 4 | general features of time series in geodey | | | Week 5 | decomposition of time series | | | Week 6 | Trends in time series data, parametric methods | | | Week 7 | Trends in time series data, non-parametric methods | | | Week 8 | Understanding moving average models | | | Week 9 | Mid-term exam | | | Week 10 | Implementing ARMA time series models | | | Week 11 | Understanding how periodograms are used with time series data | | | Week 12 | Frequency domain time series analysis | | | Week 13 | Frequency domain time series analysis | | | Week 14 | Correlation in time series | | | Week 15 | Outlier detection in time series | | | Week 16 | Final Exam | | | |
| 1 | G.E.P. Box, G. Jenkins, Time Series Analysis, Forecasting and Control, Holden-Day, San Francisco, CA, 1970. | | | |
| 1 | Peter J. Brockwell, Richard A. Davis, "Introduction to Time Series and Forecasting" (Springer Texts in Statistics) 2nd Edition,Springer, 2002 | | | 2 | Peter J. Brockwell, Richard A. Davis, "Time Series: Theory and Methods (Springer Series in Statistics)", Springer, 1991 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Homework/Assignment/Term-paper | 10
11
12
13
14
15 | | 6 | 20 | | End-of-term exam | 16 | | 2 | 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 | 3 | 14 | 42 | | Sınıf dışı çalışma | 2 | 5 | 10 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 1 | 8 | 8 | | Arasınav | 2 | 1 | 2 | | Uygulama | 0 | 0 | 0 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 1 | 6 | 6 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 1 | 14 | 14 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 84 |
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