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This entry is from Winter semester 2020/21 and might be obsolete. No current equivalent could be found.

CS 691 — Temporal Data Mining
(dt. Temporales Data Mining)

Level, degree of commitment Specialization 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)
Credit points,
formal requirements
6 CP
Course requirement(s): 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
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science.
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

Qualification Goals

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.


Module imported from M.Sc. Data Science.

It can be attended at FB12 in study program(s)

  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics
  • M.Sc. Business Informatics
  • M.Sc. Business Mathematics
  • LAaG Computer Science

When studying B.Sc. Computer Science, this module can be attended in the study area Compulsory Elective Modules in Computer Science.

The module is assigned to Practical Computer Science. Further information on eligibility can be found in the description of the study area.

Recommended Reading

  • 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.

Please note:

This page describes a module according to the latest valid module guide in Winter semester 2020/21. 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:

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.