Course: Modelling Temporal and Spatial Processes

Course type: elective
Lecturer: Damijana Kastelec, Ph.D., Associate Professor

Study programme and level Study field Academic year Semester
Applied statistics, second level All modules 1st or 2nd 1st or 2nd

For the timeline see Curriculum.


  • Prerequisites for the presence on lectures is entry to study.
  • Prerequisites for the exam are positive degree from laboratory work and seminar.

Content (Syllabus outline):

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.

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.

Intended learning outcomes:
Understanding of basic concepts of statistical analysis in time and space. Modelling of time series and spatial data, assumptions, subject-matter interpretation.


Main contact:
e-mail: info.stat (at)

Contact for administrative questions (enrolment, technical questions):
Katarina Erjavec Drešar
University of Ljubljana, Faculty of electrical engineering, Tržaška cesta 25, 1000 Ljubljana.
room num.: AN012C-ŠTU
phone: 01 4768 209
e-mail: katarina.erjavec-dresar (at)