The main purpose of the course is that the student should be well aquainted with the basic notions, theory, models and methods for solutions, in time series analysis and spatial statistics. The course covers models for time dependent or spatially dependent data. Such data frequently occurs in financial (e.g. the price development of a merchandise) and scientific (e.g. metheorological observations, radar signales) applications.
The course consists of two parts.
Module 1 (6,5 hp) Theory. The module consists of the general theory of time series, stationary and non-stationary models, e.g. ARMA- and ARIMA-models, prediction of time series, spectral theory, parameter estimation, spectrum and filtration. The part also covers methods for measuring spatial dependence (variogram, covariogram), and techniques for spatial interpolation, especially kriging.
Module 2 (1 hp) Lab Assignments. The module consists of analysis of time series and spatial data using suitable software
