Time Series Analysis 21-25th February, online block course

Lecturers: Thomas Laepple


Time series, a sequence of observations given at a sequence of time points, are ubiquitous in geosciences. Two main branches of time series analysis exist. Methodologies applied in the time domain and frequency domain analysis tools (‘spectral analysis’). In this course you will learn the basic concepts of time series analysis using both complementary approaches. A special focus is on the application of these methods to geological or climate related datasets also discussing pitfalls and caveats.

The teaching format is a mixture of theoretical lessons, hands on exercises and analysis of your own datasets.

This year, the course will be an online course; Using break-out rooms and 2-3 lecturers / persons assisting the exercises will ensure a good supervision.


At the end of this course you are able to understand the basics and apply linear time-series modelling, spectral analysis and the exploratory analysis of time-series using tools in the time and frequency domain. You will be able to perform these analyses using the statistical software R and to better judge published results involving time-series analysis.


The exam will be an homework assignment applying the methods learned on individual datasets and delivering a report
and the code (in your programming language of choice)

Exam Form:

schriftlicher Bericht


Shumway, R. H., & Stoffer, D. S. (2017). Time Series Analysis and Its Applications: With R Examples (4th ed.). Springer International Publishing. https://doi.org/10.1007/978-3-319-52452-8


1st SWS: Characteristics of Time Series & Exploratory Data Analysis. Computer lab: Introduction into time-series analysis with R
2nd SWS: Time-series models, regression, linear filters. Computer lab: Time-series analysis on own or supplied datasets
3rd SWS: Fourier transform & power spectrum. Computer lab: : Numerical experiments spectral vs. time domain
4th SWS: Spectral estimation, strategies and pitfalls. Computer lab: : spectral analysis on own or supplied datasets
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