An artificial neural network (ANN) based on particle swarm optimization (PSO) was developed for the time series prediction. The hybrid ANN+PSO algorithm was applied on Mackey-Glass chaotic time series in the short-term x(t + 6). The performance prediction was evaluated and compared with other studies available in the literature. Also, we presented properties of the dynamical system via the stud...

The visibility algorithm has been recently introduced as a mapping between time series and complex networks. This procedure allows us to apply methods of complex network theory for characterizing time series. In this work we present the horizontal visibility algorithm, a geometrically simpler and analytically solvable version of our former algorithm, focusing on the mapping of random series (se...

This paper proposes a simple methodology to construct an iterative neural network which mimics a given chaotic time series. The methodology uses the Gamma test to identify a suitable (possibly irregular) embedding of the chaotic time series from which a one step predictive model may be constructed. This model is then iterated to produce a close approximation to the original chaotic dynamics. Ha...

In order to improve the prediction accuracy of chaotic time series, a chaotic time series forecasting method based on online weighted least squares support vector machine regression (WLS-SVM) is proposed. In this method, a sliding time window is built and data in the sliding time window are employed to construct the dynamic model of a system. The model of the system is refreshed on-line with th...

Our project aims at testing various measures of chaos in time series data from an electrocardiogram (EKG). We examine data from three types of patients who we assume to produce varying EKG dynamics. We use time-delay embedding and calculate correlation dimensions to determine if the data is chaotic or random. Our results indicate that there is some evidence of deterministic chaotic behavior in ...

A test for deterministic dynamics in a time series data, namely the 0–1 test (Gottawald and Melbourne, 2004, 2005), is used to study the magnetospheric dynamics. The data, corresponding to the same time period, of the auroral electrojet index AL and the magnetic field component Bz of the solar wind magnetic field measured at 1 AU are used to compute the parameter K , which is zero for non-chaot...

This paper presents the result of the investigation of chaotic oscillator synchronization. A new approach for detecting of synchronized behaviour of chaotic oscillators has been proposed. This approach is based on the analysis of different time scales in the time series generated by the coupled chaotic oscillators. This approach has been applied for the coupled Rössler and Lorenz systems.

Abstract—Chaotic analysis has been performed on the river flow time series before and after applying the wavelet based de-noising techniques in order to investigate the noise content effects on chaotic nature of flow series. In this study, 38 years of monthly runoff data of three gauging stations were used. Gauging stations were located in Ghar-e-Aghaj river basin, Fars province, Iran. Noise le...

This paper proposes a chaotic time series model of server systemwide energy consumption to capture the dynamics present in observed sensor readings of underlying physical systems. Based on the chaotic model, we have developed a real-time predictor that estimates actual server energy consumption according to its overall thermal envelope. This chaotic time series regression model relates processo...