Deterministic chaos provides a novel framework for the analysis of
irregular time series. Traditionally, nonperiodic signals are modeled by
linear stochastic processes. But even very simple chaotic dynamical
systems can exhibit strongly irregular time evolution without random
inputs. Chaos theory offers completely new concepts and algorithms for
time series analysis which can lead to a thorough understanding of the
signal. The book introduces a broad choice of such concepts and methods,
including phase space embeddings, nonlinear prediction and noise
reduction, Lyapunov exponents, dimensions and entropies, as well as
statistical tests for nonlinearity. Related topics like chaos control,
wavelet analysis and pattern dynamics are also discussed. Applications
range from high quality, strictly deterministic laboratory data to
short, noisy sequences which typically occur in medicine, biology,
geophysics or the social sciences. All material is discussed and
illustrated using real experimental data.