Another difficulty is a vast quantity of information collected in space experiments. Classical methods of information processing are therefore not fully usable in space research. The present project is aimed to search for new information processing techniques, with a potential for solving typical problems of space physics, and to implement them in actual problems. The main area of interest is analysis of multivariate time series, both linear and non-linear.
Frequency analysis based on wavelets is implemented on different kinds of data.
Methods for diagnostics of chaotic processes, both univariate and multivariate are tested and software for the operational use of these methods is developed. Methods of analysis of point processes, such as particle and photon counts, are developed. This part of the project comprises the neural network technology and methods to search for causality. Neural networks were used for modeling of different space processes and for automatic, objective categorization of complex data. Causal modeling technique is used to search for causal relations in complex and fuzzy data.