PDDA offers a geometric reformulation that unifies rescaled-range and mean-squared-displacement scaling for Hurst exponent estimation and extends it to multivariate isotropic and anisotropic long-memory processes.
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Pairwise Distance-Diffusion Analysis (PDDA): A Geometric Framework for Estimating Hurst Exponents in Multivariate Long-Memory Processes
PDDA offers a geometric reformulation that unifies rescaled-range and mean-squared-displacement scaling for Hurst exponent estimation and extends it to multivariate isotropic and anisotropic long-memory processes.