Subject description
Time series analysis
- Exploratory time series analysis: graphical presentations, time series components, autocorrelation.
- Stationary time series modelling: ARMA models.
- Nonstationary time series modelling: ARIMA models.
Spatial statistics
- Exploratory spatial data analysis, graphical presentation; spatial correlation (variogram, spatial anisotropy).
- Spatial proceses modelling (variogram models, kriging).
- Geostatistical simulations.
Examples in R program environment.
The subject is taught in programs
Objectives and competences
Statistical modelling of processes in time or space is an important part of research in economy, ecology, epidemiology, social sciences and elsewhere. Students will learn basic methods for modelling time series and spatial data.
Teaching and learning methods
Lectures and lab classes, home-works. Lectures and labs are in the computer classrooms where students use the theoretical knowledge on the real data.
Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.
Expected study results
Understanding of basic concepts of statistical analysis in time and space. Modelling of time series and spatial data, assumptions, subject-matter interpretation.
Basic sources and literature
Shumway R. H., Stoffer D.S., 2016. Time Series Analysis and Its Applications With R Examples
Fourth Edition, Springer
Brockwell P. J., Davis R. A., 2002: Introduction to Time Series and Forecasting, Second edition, Springer
Chiles J. P., Delfiner P., 1999: Geostatistics, Modeling Spatial Uncertainity, Wiley.
Diggle P. J., Ribeiro P. J., 2006: Model-based Geostatistics, Springer Verlag.
Bivand R. S., Pebesma E. J., Gómez-Rubio V., 2008: Applied Spatial Data Analysis with R (Use R), Springer Verlag.
Kastelec D. (2020): Študijsko gradivo (pdf datoteke).
Stay up to date
University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana