Universität Klagenfurt, V.1.27
Fakultät für Technische Wissenschaften
Extracting chaotical and stochastic parts of information from time series needs very specific techniques. Motivated by two applications, image processing for cancer discrimination and methane emissions modelling we will explain the necessary techniques for statistical learning on chaotical and stochastic parts from data.
In particular, Tsallis Entropy will be introduced and its role in information theory for dynamical system explained. Iterated function systems will be used as an example for chaos re-simulation. Construction of stochastic fractals will be discussed. We will show the importance of decomposition of data to stochastic, deterministic and chaotic part.
Prof. Milan Stehlík
Christian Timmerer (christian [dot] timmerer [at] itec [dot] aau [dot] at)