Analysis of Time Series (Contract Education)
Syllabus, Master's level, 8MS014
This course has been discontinued.
- Code
- 8MS014
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Mathematics A1N
- Grading system
- Pass with distinction (5), Pass with credit (4), Pass (3), Fail (U)
- Finalised by
- The Faculty Board of Science and Technology, 28 January 2013
- Responsible department
- Department of Mathematics
Entry requirements
120 credit points including Inference Theory, or Probability and Statistics and Stochastic Modelling
Learning outcomes
In order to pass the course (grade 3) 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.
Content
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.
Instruction
Lectures, problem solving sessions and computer-assisted laboratory work.
Assessment
Written examination (8 credit points) at the end of the course. Assignments and laboratory work (2 credit points) during the course.