Chasing nonlinearity in experimental time series
Michele Castelluzzo PhD student /University of Trento
Many physical systems of interest in nowadays research, like human brain and climate, produce signals showing irregular behaviour. In this case, usual methods of study, like spectral analysis, fail to capture all valuable informations if the underlying dynamics of the system is not linear. Nonlinear time series analysis comprises a set of methods, stemming from dynamical systems theory, to analyze experimental data.
In my presentation, I will show some of the work I conducted in my lab regarding the development of new algorithms and some applications on experimental data.