Detecting Huntington Patient Using Chaotic Features of Gait Time Series

Detecting unstable periodic orbits from transient chaotic time series

We address the detection of unstable periodic orbits from experimentally measured transient chaotic time series. In particular, we examine recurrence times of trajectories in the vector space reconstructed from an ensemble of such time series. Numerical experiments demonstrate that this strategy can yield periodic orbits of low periods even when noise is present. We analyze the probability of f...

Parameter estimation using chaotic time series

A B S T R A C T We show how the response of a chaotic model to temporally varying external forcing can be efficiently tuned via parameter estimation using time series data, extending previous work in which an unforced climatologically steady state was used as the tuning target. Although directly fitting a long trajectory of a chaotic deterministic model to a time series of data is generally not...

Chaotic Time Series Prediction Using Wavelet Decomposition

A novel approach to chaotic time series prediction is proposed. It is based on the use of the Discrete Wavelet Transform for obtaining a proper decomposition of the original sequence and standard multilayer neural networks for performing the prediction of the individual components. Simulation results for the case of chaotic signals obtained by integrating the Lorenz equations are presented, and...

Chaotic Time Series Prediction Using Data Fusion

One of the main problems in chaotic time series prediction is that the underlying nonlinear dynamics is usually unknown. Using a nonlinear predictor to predict a chaotic time series usually puts a limit on the accuracy since the nonlinear predictor is basically an approximation of the unknown nonlinear mapping. In this paper, we propose using fusion of predictors as a method to improve the perf...

Detecting topological features of chaotic fluid flow.

Chaotic Time Series Analysis

Chaotic dynamical systems are ubiquitous in nature and most of them does not have an explicit dynamical equation and can be only understood through the available time series. We here briefly review the basic concepts of time series and its analytic tools, such as dimension, Lyapunov exponent, Hilbert transform, and attractor reconstruction. Then we discuss its applications in a few fields such ...