The overall purpose of the course is that the student should be well acquainted with basic concepts, theory, models, and solution methods in time series analysis, models for "dependent" data, especially ARMA-models. Such data are common in economical (e.g., the price development of a product) and in natural science (e.g., meteorological observations) applications. Further, statistical problems of identification, validation, and forecasting for selected ARMA-model and observed data are studied and as well as some generalizations for non-stationary models, like ARIMA-models. Kalman-filters, multivariate time-series, financial ARCH-models are also considered. The part of the course is some methods and techniques for statistical analysis of spatially "dependent" data, such as methods to measure spatial dependency and techniques for spatial interpolating, especially kriging. All methods are illustrated by real data examples in the corresponding Matlab/R programs. An obligatory laboration is part of the course
In a degree, this course may not be included together with another course with a similar content. If unsure, students should ask the Director of Studies in Mathematics and Mathematical Statistics. The course can also be included in the subject area of computational science and engineering.