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functions for mfdfa with other detrending procedures  (MathWorks Inc)


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    MathWorks Inc functions for mfdfa with other detrending procedures
    q -order RMS Fq(nq,:) and corresponding regression line qRegLine{nq} computed by <t>MFDFA</t> <t>(i.e.,</t> <t>Matlab</t> code and ) for time series multifractal (A), monofractal (B), and whitenoise (C) . (A) The scaling functions Fq ( blue dots ) and corresponding regression slopes Hq ( blue dashed lines ) are q -dependent. (B,C) The scaling functions Fq ( red and turqouish dots ) and regression slope Hq ( red and turqouish dashed lines ) are q -independent. (D) The q -order Hurst exponent Hq for the time series multifractal ( blue trace ), monofractal ( red trace ) and whitenoise ( turqouish trace ) where the colored dots represents the slopes Hq for q = −3, −1, 1, and 3 illustrated in (A–C) . Notice that the intercept of Hq for multifractal and monofractal time series [intercept between blue and red trace in (D) ] are close to q = 2. This intercept reflects the similarity between their overall RMS, F , in Figure .
    Functions For Mfdfa With Other Detrending Procedures, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/functions for mfdfa with other detrending procedures/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    functions for mfdfa with other detrending procedures - by Bioz Stars, 2026-05
    90/100 stars

    Images

    1) Product Images from "Introduction to Multifractal Detrended Fluctuation Analysis in Matlab"

    Article Title: Introduction to Multifractal Detrended Fluctuation Analysis in Matlab

    Journal: Frontiers in Physiology

    doi: 10.3389/fphys.2012.00141

    q -order RMS Fq(nq,:) and corresponding regression line qRegLine{nq} computed by MFDFA (i.e., Matlab code and ) for time series multifractal (A), monofractal (B), and whitenoise (C) . (A) The scaling functions Fq ( blue dots ) and corresponding regression slopes Hq ( blue dashed lines ) are q -dependent. (B,C) The scaling functions Fq ( red and turqouish dots ) and regression slope Hq ( red and turqouish dashed lines ) are q -independent. (D) The q -order Hurst exponent Hq for the time series multifractal ( blue trace ), monofractal ( red trace ) and whitenoise ( turqouish trace ) where the colored dots represents the slopes Hq for q = −3, −1, 1, and 3 illustrated in (A–C) . Notice that the intercept of Hq for multifractal and monofractal time series [intercept between blue and red trace in (D) ] are close to q = 2. This intercept reflects the similarity between their overall RMS, F , in Figure .
    Figure Legend Snippet: q -order RMS Fq(nq,:) and corresponding regression line qRegLine{nq} computed by MFDFA (i.e., Matlab code and ) for time series multifractal (A), monofractal (B), and whitenoise (C) . (A) The scaling functions Fq ( blue dots ) and corresponding regression slopes Hq ( blue dashed lines ) are q -dependent. (B,C) The scaling functions Fq ( red and turqouish dots ) and regression slope Hq ( red and turqouish dashed lines ) are q -independent. (D) The q -order Hurst exponent Hq for the time series multifractal ( blue trace ), monofractal ( red trace ) and whitenoise ( turqouish trace ) where the colored dots represents the slopes Hq for q = −3, −1, 1, and 3 illustrated in (A–C) . Notice that the intercept of Hq for multifractal and monofractal time series [intercept between blue and red trace in (D) ] are close to q = 2. This intercept reflects the similarity between their overall RMS, F , in Figure .

    Techniques Used:

    The time series multifractal (upper panel), monofractal (middle panel), and whitenoise (lower panel) are shown as blue traces . They are examples of noise like time series used in the present tutorial. All time series contain 8000 data samples each where the sample numbers are indicated by the horizontal axis. Matlab code converts the noises ( blue traces ) to random walks ( red traces ) that have a picture-in-picture similarity (subplot in the upper panel). Notice that the time series multifractal has distinct periods with small and large fluctuations in contrast to time series monofractal and whitenoise . The aim of this section is to introduce MFDFA that quantify the structure of fluctuations within the periods with small and large fluctuations.
    Figure Legend Snippet: The time series multifractal (upper panel), monofractal (middle panel), and whitenoise (lower panel) are shown as blue traces . They are examples of noise like time series used in the present tutorial. All time series contain 8000 data samples each where the sample numbers are indicated by the horizontal axis. Matlab code converts the noises ( blue traces ) to random walks ( red traces ) that have a picture-in-picture similarity (subplot in the upper panel). Notice that the time series multifractal has distinct periods with small and large fluctuations in contrast to time series monofractal and whitenoise . The aim of this section is to introduce MFDFA that quantify the structure of fluctuations within the periods with small and large fluctuations.

    Techniques Used: Introduce



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    functions for mfdfa with other detrending procedures  (MathWorks Inc)
    90
    MathWorks Inc functions for mfdfa with other detrending procedures
    q -order RMS Fq(nq,:) and corresponding regression line qRegLine{nq} computed by <t>MFDFA</t> <t>(i.e.,</t> <t>Matlab</t> code and ) for time series multifractal (A), monofractal (B), and whitenoise (C) . (A) The scaling functions Fq ( blue dots ) and corresponding regression slopes Hq ( blue dashed lines ) are q -dependent. (B,C) The scaling functions Fq ( red and turqouish dots ) and regression slope Hq ( red and turqouish dashed lines ) are q -independent. (D) The q -order Hurst exponent Hq for the time series multifractal ( blue trace ), monofractal ( red trace ) and whitenoise ( turqouish trace ) where the colored dots represents the slopes Hq for q = −3, −1, 1, and 3 illustrated in (A–C) . Notice that the intercept of Hq for multifractal and monofractal time series [intercept between blue and red trace in (D) ] are close to q = 2. This intercept reflects the similarity between their overall RMS, F , in Figure .
    Functions For Mfdfa With Other Detrending Procedures, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/functions for mfdfa with other detrending procedures/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    functions for mfdfa with other detrending procedures - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    q -order RMS Fq(nq,:) and corresponding regression line qRegLine{nq} computed by MFDFA (i.e., Matlab code and ) for time series multifractal (A), monofractal (B), and whitenoise (C) . (A) The scaling functions Fq ( blue dots ) and corresponding regression slopes Hq ( blue dashed lines ) are q -dependent. (B,C) The scaling functions Fq ( red and turqouish dots ) and regression slope Hq ( red and turqouish dashed lines ) are q -independent. (D) The q -order Hurst exponent Hq for the time series multifractal ( blue trace ), monofractal ( red trace ) and whitenoise ( turqouish trace ) where the colored dots represents the slopes Hq for q = −3, −1, 1, and 3 illustrated in (A–C) . Notice that the intercept of Hq for multifractal and monofractal time series [intercept between blue and red trace in (D) ] are close to q = 2. This intercept reflects the similarity between their overall RMS, F , in Figure .

    Journal: Frontiers in Physiology

    Article Title: Introduction to Multifractal Detrended Fluctuation Analysis in Matlab

    doi: 10.3389/fphys.2012.00141

    Figure Lengend Snippet: q -order RMS Fq(nq,:) and corresponding regression line qRegLine{nq} computed by MFDFA (i.e., Matlab code and ) for time series multifractal (A), monofractal (B), and whitenoise (C) . (A) The scaling functions Fq ( blue dots ) and corresponding regression slopes Hq ( blue dashed lines ) are q -dependent. (B,C) The scaling functions Fq ( red and turqouish dots ) and regression slope Hq ( red and turqouish dashed lines ) are q -independent. (D) The q -order Hurst exponent Hq for the time series multifractal ( blue trace ), monofractal ( red trace ) and whitenoise ( turqouish trace ) where the colored dots represents the slopes Hq for q = −3, −1, 1, and 3 illustrated in (A–C) . Notice that the intercept of Hq for multifractal and monofractal time series [intercept between blue and red trace in (D) ] are close to q = 2. This intercept reflects the similarity between their overall RMS, F , in Figure .

    Article Snippet: Matlab functions for MFDFA with other detrending procedures are available at www.ntnu.edu/inm/geri/software .

    Techniques:

    The time series multifractal (upper panel), monofractal (middle panel), and whitenoise (lower panel) are shown as blue traces . They are examples of noise like time series used in the present tutorial. All time series contain 8000 data samples each where the sample numbers are indicated by the horizontal axis. Matlab code converts the noises ( blue traces ) to random walks ( red traces ) that have a picture-in-picture similarity (subplot in the upper panel). Notice that the time series multifractal has distinct periods with small and large fluctuations in contrast to time series monofractal and whitenoise . The aim of this section is to introduce MFDFA that quantify the structure of fluctuations within the periods with small and large fluctuations.

    Journal: Frontiers in Physiology

    Article Title: Introduction to Multifractal Detrended Fluctuation Analysis in Matlab

    doi: 10.3389/fphys.2012.00141

    Figure Lengend Snippet: The time series multifractal (upper panel), monofractal (middle panel), and whitenoise (lower panel) are shown as blue traces . They are examples of noise like time series used in the present tutorial. All time series contain 8000 data samples each where the sample numbers are indicated by the horizontal axis. Matlab code converts the noises ( blue traces ) to random walks ( red traces ) that have a picture-in-picture similarity (subplot in the upper panel). Notice that the time series multifractal has distinct periods with small and large fluctuations in contrast to time series monofractal and whitenoise . The aim of this section is to introduce MFDFA that quantify the structure of fluctuations within the periods with small and large fluctuations.

    Article Snippet: Matlab functions for MFDFA with other detrending procedures are available at www.ntnu.edu/inm/geri/software .

    Techniques: Introduce