Time Series Analysis
|Level, degree of commitment in original study programme||Advanced module, compulsory elective module|
|Forms of teaching and learning,
|Lecture (3 SWS), recitation class (1 SWS) oder
lecture (2 SWS), recitation class (2 SWS), |
180 hours (60 h attendance, 120 h private study)
Course requirement: Successful completion of at least 50 percent of the points from the weekly exercises.
Examination type: Written or oral examination
|German,The grading is done with 0 to 15 points according to the examination regulations for study course M.Sc. Business Mathematics.|
|Original study programme||M.Sc. Wirtschaftsmathematik / Mathematische Vertiefungs- und Praxismodule|
|One semester, |
Regelmäßig im Wechsel mit anderen advanced moduleen
|Person in charge of the module's outline||Prof. Dr. Hajo Holzmann|
The basic models of time series analysis are discussed with a focus on financial time series, in particular
- Trend and seasonal component, stationarity and autocorrelation
- Autoregression and autoregressive moving average models
- Long-range dependence and unit roots
- Conditional heteroscedasticity and GARCH models
- State space models and the Kalman filter
Data examples and their analysis with R are treated as illustration.
The students shall
- learn the theory and basic models of time series,
- fit them to real data with the help of the statistics software R,
- practice mathematical methods (development of mathematical intuition and its formal justification, training of the ability to abstract, proof methods),
- improve their oral communication skills in the recitation classes by practicing free speech in front of an audience and during discussion.
None. The competences taught in the following modules are recommended: either Foundations of Mathematics and Linear Algebra I and Linear Algebra II or Basic Linear Algebra, either Analysis I and Analysis II or Basic Real Analysis, Probability Theory, Internship Stochastics.
- Brockwell, P. J. und Davis, R. A. (1991) Time series: theory and methods. 2nd edn. Springer.
- Kreiß, J.-P. und Neuhaus, G. (2006) Einführung in die Zeitreihenanalyse. Springer.
- Tsay, R. S. (2005) Analysis of financial time series. 2nd ed. John Wiley & Sons
This page describes a module according to the latest valid module guide in Wintersemester 2022/23. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:
- WiSe 2016/17 (no corresponding element)
- SoSe 2018 (no corresponding element)
- WiSe 2018/19
- WiSe 2019/20
- WiSe 2020/21
- SoSe 2021
- WiSe 2021/22
- WiSe 2022/23
The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.
The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.