- Contents
- 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.
- Didactic Aims
- 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.
- Target Group
- Doctoral Researchers
Postdocs & Senior Scientists
- Required Prior Knowledge
- Must already have a working knowledge of Matlab or R (e.g. read/write data, work with matrices).
- Organisational Info
- Date: 17.04.2019 09:00 – 18.04.2019 17:00
Course Duration in Days: 2
Location: UFZ Leipzig: building 1, lecture hall/Vortragssaal
Course Language: English
Registration Deadline: 03.04.2019
Cancellation Deadline: 03.04.2019
Participation fee for externals (net): 0.00 €
Focus on scientific exchange: Yes
Eligibility for participation: 1.70
Need help? Contact: josephine.mitze@ufz.de / 0341 235 4652
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