651-3660-00L Analysis of Time Series in Environmental Physics and Geophysics

Abstract	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