. We report the results of time series analysis of human body sway while quiet upright stance. The bivariate records (stabilograms) are measured by means of a force plate. To investigate interrelations between oscillations in anterior--posterior and lateral directions we use several techniques: cross-spectrum analysis, generalized mutual information, and calculation of instantaneous relative phase. We find that the stabilograms can be qualitatively rated into two groups: noisy and oscillatory patterns. Further, we show that oscillatory patterns may demonstrate phase locking. We argue that these patterns are due to stochastic and chaotic dynamics, respectively. We discuss the plausible strategy of postural control and present the model that qualitatively describes transitions from noisy to oscillatory patterns and phase synchronization. The relevance of the results of the time series analysis for the diagnostics of neurological pathologies is discussed. In: Nonlinear Analysis of Physio...

I, the undersigned, hereby declare that the work contained in this thesis is my own original work and that I have not previously in its entirety or in part submitted it at any university for a degree.

This thesis explores the use of neural networks for predicting difficult, real-world time series. We first establish and demonstrate methods for characterising, modelling and predicting well-known systems. The real-world system we explore is seismic event data obtained from a South African gold mine. We show that this data is chaotic. After preprocessing the raw data, we show that neural networks are able to predict seismic activity reasonably well.