Universität Wien
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300491 UE Selected Statistical Methods: Time Series Analysis (2022W)

1.00 ECTS (1.00 SWS), SPL 30 - Biologie
Continuous assessment of course work
ON-SITE

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 10 participants
Language: German

Lecturers

Classes (iCal) - next class is marked with N

  • Tuesday 04.10. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1 (Kickoff Class)
  • Tuesday 11.10. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 18.10. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 25.10. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 08.11. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 15.11. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 22.11. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 29.11. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 06.12. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 13.12. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 10.01. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 17.01. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 24.01. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1
  • Tuesday 31.01. 16:45 - 20:00 Seminarraum 1.8, Biologie Djerassiplatz 1, 1.007, Ebene 1

Information

Aims, contents and method of the course

Topics: Time series analysis. ML (Maximum Likelihood) methods; Heuristic description of time series; Basics of Sampling theory; various smoothing methods (window smoothing; interpolation methods, regression, Laplace smoothing); analysis of autocorrelation; Fourier series, Fourier coefficients, background noise; Multivariate time series: SVD (singular value decomposition); Introduction to CLustering; NN (neural networks) and AI (Artifical intelligence) Lectures will be held in blocks. Basic knowledge of statistics (the concept of a distribution and of a significance level) desirable. The necessary mathematical tools will be introduced in the context of the practice problems.

Assessment and permitted materials

1. Periodic questionnaires
2. A project (chosen by the student) will be presented by the student and evaluated.

Minimum requirements and assessment criteria

1. Quality of responses to the questionnaires
2. Quality of the presented project.

ad 1 (Questionnaires): 30%
ad 2 (project presenttion) 70%

passing grade: 60% or above

Examination topics

1. Defined by the material presented in the examples

Reading list

Key words in WIKIPEDIA

Association in the course directory

MAN 3

Last modified: Mo 23.01.2023 14:09