Chaos Time Series Analysis and Prediction Toolbox

This documentation introduces the Chaos Time Series Analysis and Prediction Toolbox, which incorporates numerous chaos time series analysis techniques and prediction methods alongside extensive additional functionalities. Key features include: data visualization and preprocessing capabilities using MATLAB's plotting functions, analytical processing through algorithms like phase space reconstruction (embedding dimension optimization via false nearest neighbors method), Lyapunov exponent calculation, and correlation dimension estimation. The toolbox supports custom report generation through automated scripting interfaces and includes comprehensive tutorials with code examples demonstrating practical implementation of prediction algorithms such as local linear prediction and neural network approaches. Furthermore, detailed help documentation provides parameter configuration guidance for functions like time delay embedding and chaos characteristic identification. Overall, this toolbox represents a powerful yet user-friendly solution suitable for diverse application scenarios in nonlinear dynamics research and time series forecasting.