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

Level, degree of commitment Specialization module, depends on importing study program
Forms of teaching and learning,
workload		 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
Language,
Grading		 English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Data Science.
Origin M.Sc. Data Science
Duration,
frequency		 One semester,
irregular
Person in charge of the module's outline Prof. Dr. Michael Thrun

Contents

  • 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

  • can explain and apply scientific procedures in the investigation of time series in order to discover new and previously unknown temporal patterns,
  • can describe and compare the most important analysis methods such as Fourier and wavelet analysis,
  • can explain statistical modeling options for time series,
  • can apply methods to generate symbolic pattern descriptions from time series,
  • can apply scientific working methods (recognizing, formulating, solving problems, training the ability to abstract),
  • are able to speak freely about scientific content, both in front of an audience and in a discussion.

Prerequisites

None. The competences taught in the following modules are recommended: Object-oriented Programming, Algorithms and Data Structures.


Applicability

The module can be attended at FB12 in study program(s)

  • B.Sc. Data Science
  • B.Sc. Computer Science
  • M.Sc. Data Science
  • M.Sc. Computer Science

When studying M.Sc. Data Science, this module can be attended in the study area Free Compulsory Elective Modules.

The module can also be used in other study programs (export module).

The module is assigned to 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 2025/26. 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.