On completion of the course, the student should be able to:
give an account for the concepts stationary time series and autocorrelation and know how to estimate autocorrelation based on an observed time series;
apply methods for estimation of trend and seasonal variation in time series;
estimate parameters of ARIMA-processes and assess the validity of the fitted models.
make predictions, in particular for ARIMA-processes;
explain the foundations of spectral theory and how to estimate spectral density;
evaluate results from statistical computer software (for example R) for model fitting of time series.
Stationary time series. ARIMA processes. Box-Jenkin's method for model adaptation. Prediction. Seasonal modelling. Spectral theory, smoothing methods for spectral estimation. Software for analysis of time series. Overview of multivariate models, Kalman-filters och non-linear models such as ARCH- and GARCH-models.
Lectures, problem solving sessions and computer-assisted laboratory work.
Written examination (8 credit points) at the end of the course. Assignments and laboratory work (2 credit points) during the course.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.