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| IST7041 | Time Series Analysis | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of STATISTICS and COMPUTER SCIENCES | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Erdinç KARAKULLUKÇU | | Co-Lecturer | None | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To introduce a variety of statistical models for time series and cover the main methods for analysing these models. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Compute and interpret a correlogram and a sample spectrum | 7,8 | 1,3 | | PO - 2 : | Compute forecasts for a variety of linear methods and models. | 7,8 | 1,3 | | 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 | | |
| Basic concepts, auto-covariance, auto-correlation and partial autocorrelation functions, white noise process, stationary and non-stationary time series models, AR (p), MA (q), ARMA (p, q) and ARIMA models, seasonal time series (SARIMA), model identification, input estimation, the appropriate and best model selection, simulation studies of time series, unit root tests, the transmission function, introduction to multivariate time series analysis. Convolution method and properties. An introduction to spectral analysis, Fourier transform, derivative and integral in Fourier environmen; |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Basic concepts, | | | Week 2 | auto-covariance, auto-correlation and partial autocorrelation functions, | | | Week 3 | white noise process, stationary and non-stationary time series models, | | | Week 4 | AR (p), MA (q), ARMA (p, q) and ARIMA models, | | | Week 5 | seasonal time series (SARIMA), | | | Week 6 | model identification, parameter estimation, | | | Week 7 | the appropriate and best model selection, | | | Week 8 | Mid-term exam | | | Week 9 | Windows and smoothing | | | Week 10 | simulation studies of time series, | | | Week 11 | unit root tests, the transmission function, | | | Week 12 | introduction to multivariate time series analysis. | | | Week 13 | Convolution method and properties. | | | Week 14 | An introduction to spectral analysis, | | | Week 15 | Fourier transform, derivative and integral in Fourier environmen;Spectral density
| | | Week 16 | End-of-term exam | | | |
| 1 | Edward J. Wegman, 1998, Time Series Analysis: Theory, Data Analysis and Computation | | | |
| 1 | D.E. Newland,1975, An introduction to Random Vibration and Spectral Analysis, Longman, London | | | 2 | Piet M.T. Broersen, 2006, Automatic Autocorrelation and Spectral Analysis, Springer, London | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 8 | 02/04/2010 | 1 | 30 | | Homework/Assignment/Term-paper | 0611 | | 4 | 20 | | End-of-term exam | 16 | 29/05/2010 | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Ödev | 6 | 12 | 72 | | Total work load | | | 72 |
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