Detrended Flucatuation Analysis (DFA) of Long-range temporal correlations

Version 1.1.0.0 (6,89 ko) par Simon-Shlomo Poil
The DFA algorithm is a scaling analysis method used to estimate long-range temporal correlations.
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Mise à jour 31 jan. 2013

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The DFA algorithm is a scaling analysis method used to estimate long-range temporal correlations.

Description: The Detrended flctuation analsysis (DFA) algorithm is a scaling analysis method used to estimate long-range temporal correlations of power-law form. In other words, if a sequence of events has a non-random temporal structure with slowly decaying auto-correlations, DFA can quantify how slowly these correlations decay as indexed by the DFA power-law exponent.

We here present the DFA algorithm implemented as a Biomarker for the Neurophysiological Biomarker Toolbox. You can download this toolbox at http://www.nbtwiki.net.

A tutorial on Detrended fluctuation analysis can be found here: http://www.nbtwiki.net/doku.php?id=tutorial:detrended_fluctuation_analysis_dfa

Citation pour cette source

Simon-Shlomo Poil (2024). Detrended Flucatuation Analysis (DFA) of Long-range temporal correlations (https://www.mathworks.com/matlabcentral/fileexchange/40011-detrended-flucatuation-analysis-dfa-of-long-range-temporal-correlations), MATLAB Central File Exchange. Récupéré le .

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Créé avec R2008a
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NBT_DFA/nbt_Biomarker/

NBT_DFA/nbt_DFA/

Version Publié le Notes de version
1.1.0.0

I corrected a missing 'd' in the title
1.0.0.0