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.
Understanding of various methods for the analysis of time-dependent data.
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 and exercises are made available.
- 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 information (valid until the course unit is held again)