Multiple Approaches for Calculating Hurst Exponent in Time Series Analysis

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Resource Overview

While various methods exist for computing Hurst exponents in time series, wavelet-based approaches offer superior accuracy. This MATLAB implementation specifically utilizes wavelet transforms to calculate Hurst coefficients, providing robust algorithmic solutions for fractal analysis.

Detailed Documentation

Several methodologies are available for calculating Hurst exponents in time series analysis, with wavelet-based methods representing one of the most accurate approaches. This MATLAB implementation features a wavelet-transform algorithm for Hurst coefficient computation, employing multi-resolution decomposition through discrete wavelet transforms (DWT) to analyze scaling properties. The program additionally incorporates alternative computational techniques including statistical methods (utilizing rescaled range analysis) and fractal-based approaches (leveraging detrended fluctuation analysis). These diversified algorithms allow users to select appropriate methods based on specific data characteristics, enabling comprehensive time series pattern recognition through customizable parameter configurations and visualization outputs for rigorous quantitative analysis.

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