Temporal Data Mining
(dt. Temporales Data Mining)
|Level, degree of commitment in original study programme||Advanced module, compulsory elective module|
|Forms of teaching and learning,
|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 as well as at least 2 presentations of the tasks.
Examination type: Oral examination
|German,The grading is done with 0 to 15 points according to the examination regulations for study course M.Sc. Data Science.|
|Original study programme||M.Sc. Data Science / Informatik Vertiefungsmodule|
|One semester, |
each summer semester
|Person in charge of the module's outline||Prof. Dr. Alfred Ultsch|
- practical use of explorative statistical methods to describe and analyse the data
- Theory and practice of Fourier transformations for time series
- Theory and practice of wavelet transformations for time series
- Modeling Stochastic Processes (ARMA, GARCH)
- Markov Models
- Neural networks for the analysis and prognosis of time series
- Temporal Knowledge Discovery
The students shall
- learn about scientific approaches to the analysis of time series in order to discover new and previously unknown temporal patterns,
- acquire knowledge of key analytical techniques such as Fourier and wavelet analysis,
- get to know statistical statistical modelling possibilities of time series,
- learn methods to create symbolic pattern descriptions from time series,
- practice scientific working methods (recognizing, formulating, solving problems, training the ability to abstract),
- practice oral communication skills in the exercises by practicing free speech in front of an audience.
None. The competences taught in the following modules are recommended: Object-oriented Programming, either Algorithms and Data Structures or Practical Informatics II: Data Structures and Algorithms for Pre-Service-Teachers, Knowledge Discovery.
- S. Mallat: A Wavelet Tour on Signal Processing, Academic Press 1999.
- D.B. Percival, A.T Walden: Wavelet Methods for Time Series Analysis, Cambridge 2002.
- J. Franke, W. Härdle, C. Hafner: Statistik der Finanzzeitreihen, Springer 2003.
- J. Hartung, B. Elpelt: Multivariate Statistik, Oldenburg, 1999.
This page describes a module according to the latest valid module guide in Wintersemester 2021/22. 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.