105.121 Introduction to stochastic processes and time series
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2021S, UE, 1.0h, 1.5EC


  • Semester hours: 1.0
  • Credits: 1.5
  • Type: UE Exercise
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to

  • manipulate Brownian motions,
  • compute (simple) Ito integrals,
  • compute entrance time, entrance probabilities and other properties of Markov chains,
  • check the stationarity of stochastic processes,
  • compute autocovariance function and other properties of stationary processes,
  • estimate AR processes,
  • compute linear forecasts.,
  • ...

Subject of course

This course provides some further insights into the concepts and methods presented in the lecture. This problem solving course will include theoretical examples as well as applications to simulated and real world data sets.

Teaching methods

Exercise sessions.

Mode of examination




Examination modalities

The completed problems have to be marked in TUWEL. Grade distribution:

  • more than 80% "sehr gut"
  • 71-80% "gut"
  • 61-70% "befriedigend"
  • 51-60% "genügend"
  • less than 51% "nicht genügend".

Group dates

Gruppe AMon17:00 - 18:0015.03.2021 - 28.06.2021 siehe TUWEL.
Gruppe BMon18:00 - 19:0015.03.2021 - 28.06.2021 siehe TUWEL.
Gruppe CMon19:00 - 20:0015.03.2021 - 28.06.2021 siehe TUWEL.

Course registration

Use Group Registration to register.

Group Registration

GroupRegistration FromTo
Gruppe A28.01.2021 00:0007.03.2021 23:59
Gruppe B28.01.2021 00:0007.03.2021 23:59
Gruppe C28.01.2021 00:0007.03.2021 23:59



Brzezniak, Zdzislaw; Zastawniak, Tomasz Basic stochastic processes. A course through exercises. Springer Undergraduate Mathematics Series. Springer-Verlag London, Ltd., London, 1999.

Norris, J. R. Markov chains. Reprint of 1997 original. Cambridge Series in Statistical and Probabilistic Mathematics, 2. Cambridge University Press, Cambridge, 1998.

Deistler, Manfred; Scherrer, Wolfgang. Modelle der Zeitreihenanalyse. Mathematik Kompakt,  Birkhäuser, 2018.

Previous knowledge

Basic knowledge of probability theory, random variables, expectation, variance, covariance, ...

Accompanying courses


  • Attendance Required!