Permutation entropy is a relatively new promising concept for measuring complexity of time series and of the systems behind with applications in various fields. The idea of permutation entropy it is to quantify the amount of up and down in a time series on the base of considering the distribution of ordinal patterns in it. Although metric features are neglected to a great extend, this approch often preserves more information than expected on a first glance.

The aim of the talk is to explain why permutation entropy is interesting for complex data analysis and to discuss perspectives, challenges and limits of its application. Our discussion emphasizes the viewpoint of symbolic dynamics.