MATLAB Code for Nonlinear Time Series Analysis

This MATLAB code collection offers practical implementations for Nonlinear Time Series Analysis (NTSA), serving as a reference for researchers and practitioners. NTSA methodology enables the detection of nonlinear characteristics hidden within time series data, revealing underlying patterns and dynamical properties that linear analysis might overlook. The implementation includes core algorithms such as: - Phase space reconstruction using time-delay embedding methods - Calculation of Lyapunov exponents to quantify system sensitivity - Mutual information analysis for optimal time delay selection - Correlation dimension estimation for fractal dimension analysis These techniques allow deeper understanding of complex systems' behavior, with significant applications across finance (market prediction), meteorology (weather pattern analysis), and biomedical engineering (physiological signal processing). The code is structured with modular functions and includes examples demonstrating parameter configuration and result visualization. For technical assistance regarding MATLAB implementation details or algorithm customization, please feel free to contact us - we provide comprehensive support for academic and industrial applications.