Nonlinear time series analysis

Nonlinear time series analysis allows characterizing dynamical systems in which nonlinearity gives rise to a complex temporal evolution. Importantly, these nonlinear techniques can extract information from real-world experimental signals that cannot be resolved by classical linear techniques. While there is a long history of linear time series analysis, nonlinear methods have only just begun to reach maturity. Key targets of our work are the discrimination of deterministic and stochastic dynamics as well as the development of advance methods/algorithms for the classification and analysis of nonlinear response. These algorithms consist of the following exhaustive tools.

Applications are in the area of Nonlinear System Diagnostics, Machinery Dynamics, Biomedical diagnostics and systems control. Here are some specific projects.