Mathematical Methods to Extract Information from High-dimensional Data
With the aim to develop a general mathematical methodology for identifying events in high dimensional time series, the work program combines three different approximation methods. These are based on an analytic numerical approach for function approximation and dimension reduction, a stochastic approach for statistical clustering and a discrete graph-based method for generating a finite state transition graph model. In bringing these techniques together, the goal is to come up with new algorithmic methods that answer both theoretical and practical questions and support human-interpretable time series analysis.