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June 28, 2026Wiley Interdisciplinary Reviews Computational StatisticsOpen Access

Wavelet‐Based Hurst Exponent Estimation

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Authors

DVDixon VimalajeewaFRFabrizio RuggeriBVBrani Vidaković

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Overview

Review evaluates wavelet-based approaches for estimating Hurst exponent in time series, suggesting future directions for research.

Key Points

  • This review focuses on the estimation of the Hurst exponent using wavelet-based methods and their applications across various fields.
  • Explored theoretical foundations and practical implementations of wavelet methods for estimating the Hurst exponent.
  • Reviewed strengths and limitations of existing techniques in diverse applications such as biology and engineering.
  • Identified challenges in noise management and non-stationary data handling.
  • Highlighted the growing interest in integrating traditional wavelet methods with modern machine learning approaches.
  • Outlined key challenges and areas for improvement in self-similarity estimation.
  • Provided a roadmap for future research in methodological robustness and practical adoption.

Cite This Study

Vimalajeewa et al. (2026) studied this question.

synapsesocial.com/papers/6a40bb1461bb0a67205c6bafhttps://doi.org/10.1002/wics.70072
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