Chaotic Time Series Analysis and Prediction Toolbox Version 1.0

This document introduces the Chaotic Time Series Analysis and Prediction Toolbox Version 1.0 (Chaotic Time Series Analysis and Prediction MATLAB Toolbox - version 1.0). This toolbox provides a comprehensive MATLAB-based framework for analyzing and predicting chaotic time series data. The toolbox incorporates multiple analytical and predictive functions that enable researchers to study the properties and trends of chaotic time series through numerical implementations of key algorithms including phase space reconstruction using time-delay embedding, Lyapunov exponent calculation for chaos identification, and nonlinear prediction models. The toolbox supports applications across various disciplines such as finance, physics, and astronomy. As a MATLAB toolbox, it features straightforward installation procedures and an intuitive user interface designed for both novice and advanced users. The implementation includes core functions like `phaseSpaceReconstruct` for embedding dimension optimization, `lyapunovExponent` for stability analysis, and `nonlinearPredict` for forecasting chaotic systems. Additionally, the toolbox provides extensive documentation with detailed code examples, demonstration scripts, and theoretical background to help users understand the underlying methodologies and effectively apply the tools to their specific research problems. The package includes sample datasets and tutorial cases that illustrate practical implementation scenarios for different types of chaotic systems.