@inproceedings{84efbe67-a470-4f51-84ac-fbc4d095559d,
  abstract     = {{<p>Many real-world systems generate multivariate time series data, often exhibiting self-similarity and scale-invariance across different modalities. The estimation of Hurst exponents in such settings is crucial for analyzing long-range dependencies. Yet, traditional eigenanalysis-based methods suffer from scale-dependent distortions, particularly in the presence of scaling amplitude discrepancies. In this work, we propose a novel multiscale eigenanalysis approach that leverages joint diagonalization of wavelet random matrices to improve estimation accuracy. By approximating a common eigenvector basis across multiple scales, our method mitigates the limitations of scale-wise eigenvalue regressions and provides robust estimation of multivariate self-similarity parameters. We demonstrate the effectiveness of our approach through extensive Monte Carlo simulations, showcasing improved performance over traditional methods in both orthogonal and non-orthogonal mixing scenarios. These findings establish joint eigenvector-based wavelet analysis as a powerful tool for multivariate self-similarity estimation.</p>}},
  author       = {{Wendt, Herwig and Didier, Gustavo and Carlsson, Marcus and Troedsson, Erik and Abry, Patrice}},
  booktitle    = {{2025 33rd European Signal Processing Conference, EUSIPCO 2025 - Proceedings}},
  isbn         = {{9789464593624}},
  issn         = {{2219-5491}},
  keywords     = {{Hurst exponent; joint diagonalization; multivariate self-similarity; random matrices; wavelets}},
  language     = {{eng}},
  pages        = {{2617--2621}},
  publisher    = {{European Signal Processing Conference, EUSIPCO}},
  series       = {{European Signal Processing Conference}},
  title        = {{Multiscale approximate eigenvectors for multivariate self-similarity estimation}},
  url          = {{http://dx.doi.org/10.23919/EUSIPCO63237.2025.11226404}},
  doi          = {{10.23919/EUSIPCO63237.2025.11226404}},
  year         = {{2025}},
}