In geoscience we are often collecting vast digitally recorded. Such time-series data can be processed to emphasize particular aspects of the signal, an enormous advantage over paper records. We introduce fundamental tools and concepts of time-series analysis, e.g. deterministic vs stochastic processes, signal correlation, and Fourier analysis.
Learning objective
Understanding of various methods for the analysis of time-dependent data.
Content
Based on various data sets we illustrate basic principles of time series and apply different methods of analysis: deterministic and stochastic processes, stationary and non-stationary processes, sampling theorem, trend analysis, auto- and cross-correlation, frequency analysis (Fourier Transformation).
The exercises require basic knowledge of MATLAB.
Lecture notes
Lecture notes and exercises are made available.
Literature
- R. H. Shumway and D. S. Stoffer: Time Series Analysis and its Applications. Springer, New York, 2000. - W.H. Press, B.P. Flannery, S.A. Teukolsky und W.T. Wetterling: Numerical Recipes: The Art of Scientific Computing. Cambridge University Press.
Prerequisites / Notice
Prerequisites: equivalent to the first three semester of an earth science or environmental science curriculum.
Basic knowledge of matlab
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course