Menü
Positionsanzeige
Inhalt

[ 551OKMEZRAK14 ] KV Time Series Analysis (Statistics)
Versionsauswahl
(*) Unfortunately this information is not available in english.
Workload Education level Study areas Responsible person Hours per week Coordinating university
4 ECTS B2 - Bachelor's programme 2. year Statistics Helga Wagner 2 hpw Johannes Kepler University Linz
Detailed information
Pre-requisites (*)keine
Original study plan Bachelor's programme Statistics and Data Science 2025W
Learning Outcomes
Competences
Students are able to analyse time series with standard models like ARMA ARIMA and GARCH models, to interprete the results correctly and perform a residual analysis of the fitted models
Skills Knowledge
  • Knowing and understanding of the basic problems, terms and methods for the analysis of time series (k1,k2)
  • Applying and critical evaluation of methods for time series analysis (k3,k4, k5)
  • Applying methods for time series analysis with the statistic software R (k3)
  • Implementing and performing simulation studies for time series models (k2,k3)
  • Basic concepts and descriptive methods for time series analysis
  • Exponential smoothing
  • ARMA models for stationary time series
  • ARIMA models and unit root tests
  • Modelling volatility with ARCH and GARCH models
  • Analysis of time series with the statistic software R
Criteria for evaluation Exam
Project
Methods Lecture
Computer lab
Language German
Study material Cowpertwait, Paul S. P. and Metcalfe, Andrew V. (2009) Introductory time series with R

Vogel, Jürgen (2015). Prognose von Zeitreihen

Changing subject? No
Earlier variants They also cover the requirements of the curriculum (from - to)
4MSZRKV: KV Time Series Analysis (Statistics) (2011S-2014S)
On-site course
Maximum number of participants 40
Assignment procedure Assignment according to priority