Hurst multimodality detection based on large wavelet random matrices - Computational Imaging and Vision Access content directly
Conference Papers Year : 2022

Hurst multimodality detection based on large wavelet random matrices

Oliver Orejola
  • Function : Author
  • PersonId : 1151433
Gustavo Didier
  • Function : Author
  • PersonId : 1113836
Patrice Abry

Abstract

In the modern world, systems are routinely monitored by multiple sensors, generating "Big Data" in the form of a large collection of time series. In this paper, we put forward a statistical methodology for detecting multimodality in the distribution of Hurst exponents in high-dimensional fractal systems. The methodology relies on the analysis of the distribution of the log-eigenvalues of large wavelet random matrices. Depending on the presence of a single or many Hurst exponents, we show that the wavelet empirical log-spectral distribution displays one or many modes, respectively, in the threefold limit as dimension, sample size and scale go to infinity. This allows for the construction of a unimodality test for the Hurst exponent distribution. Monte Carlo simulations show that the proposed methodology attains satisfactory power for realistic sample sizes.
Fichier principal
Vignette du fichier
OrejolaDidierWendtAbry2022_EUSIPCO_2022_06_01.pdf (974.22 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03850437 , version 1 (21-07-2022)
hal-03850437 , version 2 (13-11-2022)

Identifiers

  • HAL Id : hal-03850437 , version 2

Cite

Oliver Orejola, Gustavo Didier, Patrice Abry, Herwig Wendt. Hurst multimodality detection based on large wavelet random matrices. 30th European Conference on Signal Processing (EUSIPCO 2022), European Association for Signal Processing (EURASIP), Aug 2022, Belgrade, Serbia. pp.2131-2135. ⟨hal-03850437v2⟩
99 View
57 Download

Share

Gmail Mastodon Facebook X LinkedIn More