Computational Time Series Analysis for Multidimensional Non-Stationary Data

Staff - Faculty of Informatics

Start date: 24 February 2010

End date: 25 February 2010

The Faculty of Informatics is pleased to announce a seminar given by Illia Horenko

DATE: Wednesday, February 24th, 2010
PLACE: USI Università della Svizzera italiana, room SI-15, Informatics building (Via G. Buffi 13)
TIME: 14.30

In recent years there has been considerable increase of interest in the mathematical modeling and computational analysis of complex non-stationary and non-equilibrium systems. Such systems can be found, e.g., in weather forecast (transitions between weather conditions), climate research (processes associated with global warming), fluid mechanics (transient processes between different flow regimes and interfaces) and in econometrics (e.g., switches between phases of different market dynamics). In all cases the accumulation of sufficiently detailed multidimensional time series has led to the formation of huge databases containing enormous but still undiscovered treasures of information. However, the extraction of essential information out of the data is usually hindered by the multidimensional and non-stationary nature of the signal. The standard filtering approaches have in general unfeasible numerical complexity in high dimensions, other standard methods (like f. e. Kalman-filter, MVAR, ARCH/GARCH etc.) impose some too restrictive statistical assumptions about the type of the underlying dynamics.
New non-probabilistic framework for inference of multidimensional non-stationary time series in discrete and continuous state spaces will be presented.    The approach is based on optimization of the averaged clustering functional in appropriate function spaces. The computational advantages of the presented method will be discussed in comparison to the standard stochastic approaches (like VARX(p), Hidden Markov Models (HMMs)) and statistical aspects of the data post-processing    will be discussed. The application of the computational framework will be briefly demonstrated in context of climate, turbulence, MD and sociology.

Prof. Dr. Illia Horenko is professor at the Institute of Computational Science of Universita' della Svizzera Italiana in Lugano. His researches are in the fields of time series and high-dimensional data analysis, inverse stochastic modelling and non-stationary time series analysis, data-based methods of trend identification. He is the head of a research group which is active in the development of high-performance methods for applications in fluid mechanics, meteorology, climate research, biophysics and finance.