MATLAB Source Code for Chaotic Time Series Prediction

I would like to share MATLAB source code for chaotic time series prediction, which I hope will be valuable to researchers and practitioners. Chaotic time series represent a crucial branch of nonlinear dynamics with extensive applications across various domains including finance, weather forecasting, electrocardiogram analysis, and more. Predicting the trends and behaviors of chaotic time series is fundamental to numerous research studies and practical implementations. This MATLAB implementation provides a complete framework for chaotic time series analysis and prediction, featuring key algorithms such as phase space reconstruction, Lyapunov exponent calculation, and neural network-based forecasting models. The code includes practical implementations of delay coordinate embedding methods for state space reconstruction, which is essential for capturing the underlying dynamics of chaotic systems. The source code demonstrates various prediction techniques including local linear prediction methods, artificial neural networks (specifically NARX networks), and support vector regression approaches. Each method is accompanied by parameter optimization routines and performance evaluation metrics such as root mean square error (RMSE) and prediction accuracy calculations. This implementation aims to help users better understand the entire process of chaotic time series prediction, from data preprocessing and phase space reconstruction to model training and prediction validation. The code is structured with clear comments and modular functions, making it suitable for both educational purposes and research applications. I hope this resource will provide valuable insights and practical assistance for your research projects and implementations.