Visualization provides powerful tools for the investigation of dynamical systems. The application of various visualization techniques to complex and chaotic dynamical systems is discussed. The interactive specification and modification of strange attractors allow an easier understanding of the underlying dynamics. Graphical time series analysis visualizes time series and phase space reconstruct...

Prediction on complex time series has received much attention during the last decade. This paper reviews least square and radial basis function based predictors and proposes a support vector regression (SVR) based local predictor to improve phase space prediction of chaotic time series by combining the strength of SVR and the reconstruction properties of chaotic dynamics. The proposed method is...

Interest in chaotic time series prediction has grown recent years due to its multiple applications fields such as climate and health. In this work, we summarize the contribution of works that use different machine learning (ML) methods predict series. It is highlighted challenge predicting larger horizon with low error, for task, majority authors datasets generated by systems Lorenz, Rössler Ma...