Nonlinear Time Series , Complexity Theory

A Novel Method for Detection of Epilepsy in Short and Noisy EEG Signals Using Ordinal Pattern Analysis

Introduction: In this paper, a novel complexity measure is proposed to detect dynamical changes in nonlinear systems using ordinal pattern analysis of time series data taken from the system. Epilepsy is considered as a dynamical change in nonlinear and complex brain system. The ability of the proposed measure for characterizing the normal and epileptic EEG signals when the signal is short or is...

River Discharge Time Series Prediction by Chaos Theory

The application of chaos theory in hydrology has been gaining considerable interest in recent years.Based on the chaos theory, the random seemingly series can be attributed to deterministic rules. Thedynamic structures of the seemingly complex processes, such as river flow variations, might be betterunderstood using nonlinear deterministic chaotic models than the stochastic ones. In this paper,...

Nonlinear Time Series Complexity Theory and Finance

Fractal response of physiological signals to stress conditions, environmental changes, and neurodegenerative diseases

In the past two decades the biomedical community has witnessed several applications of nonlinear system theory to the analysis of biomedical time series and the development of nonlinear dynamic models. The development of this area of medicine can best be described as nonlinear and fractal physiology. These studies have been intended to develop more reliable methodologies for understanding how b...

Hybrid Wavelet and Chaos Theory for Runoff Forecasting

-This paper introduced a method of decomposing non-stationary runoff time series. By wavelet decomposing, the runoff time series is decomposed into stationary time series and stochastic time series, and AR(n) model be imposed for forecasting stationary time series. By studying chaos characteristic of stochastic time series, this paper put forward a nonlinear chaos dynamics-forecasting model to ...