Fractional Lower Order Covariance Spectrum with MATLAB Implementation

This MATLAB program package provides comprehensive implementation of fractional lower order covariance spectrum analysis, containing four different estimation methods for calculating fractional lower order covariance spectra. Fractional lower order covariance spectrum serves as a powerful tool for time series analysis, specifically designed to measure cross-frequency correlations in signal processing applications. The program enables researchers to gain deeper insights into their data and extract additional meaningful information through various analytical approaches including: statistical property investigation of research data, future trend prediction, and outlier detection in datasets. Key implementation features include optimized matrix operations for covariance computation, efficient FFT-based spectral estimation algorithms, and customizable parameter settings for different signal characteristics. Each estimation method incorporates unique numerical techniques such as iterative optimization for fractional order parameters and robust statistical processing for handling non-Gaussian signals. If you are interested in fractional lower order covariance spectrum and its practical applications in signal processing and time series analysis, this program serves as an excellent starting point with well-documented code structure and comprehensive functional examples.