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The course provides a practical introduction to time series analyses and covers three main topics:
1) Data preparation and visualization, dealing with data gaps and methods for interpolation.
2) Finding trends and periodicities using empirical mode decomposition; methods for noise reduction and auto correlation analysis.
3) Methods for comparing two or more time series with respect to similarities, asynchronism, distance measures.
Understand what are time series, stationarity, Fourier transform, distance matrices. Perform data interpolation, empirical mode decomposition, cross-correlation, time series clustering, and dynamic time warping.
Must already have a working knowledge of Matlab or R (e.g. read/write data, work with matrices).
Participation fees do not apply to cooperation partners, i.e. PhD candidates of SENSE partner WIMEK.
The current conditions of participation and use apply.
Please note: In order to register, a personal account has to be created first.