Data on the course

5 ECTS credits / 133 hours of work

English

Not lectured every year, Period 4

After taking the course the student is expected to understand basic time series concepts and terminology, to be able to select time series methods appropriate to goals and summarize results of time series analysis in writing. The main objective of this course is to develop the skills needed to do empirical research in fields operating with time series data sets.

This is an introductory course, with particular emphasis on practical aspects of the typical time series encountered in astronomy and in related field of sciences: search for periodicities hidden in noise. Topics include detrending, filtering, autoregressive modeling, spectral analysis, regression, and wavelet analysis. Methods that can be applied to evenly and unevenly spaced time series are considered.

Face-to-face teaching

Lectures 24 h, exercises 24 h. The theoretical part of lectures concentrates on both parametric and nonparametric time series analysis methods. The practical part involves programming, application and interpretation of the results. Self-study 85 h.

Student of the intermediate and advanced level.

No pre-knowledge is required in the time series analysis field. A rough knowledge of Fourier transforms and related functions as well as some basic knowledge in Statistics would be an advantage.

No alternative course units or course units that should be completed simultaneously

Numerical Recipes, papers.

One written examination Read more about assessment criteria at the University of Oulu webpage.

Numerical grading scale 0 - 5, where 0 = fail

Vitaly Neustroev

No work placement period