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Fractional Brownian motion synthesis



    fBm = wfbm(H,L) returns a fractional Brownian motion signal fBm of the Hurst parameter H (0 < H < 1) and length L, following the algorithm proposed by Abry and Sellan [1]. By default, wfbm uses six reconstruction steps and the orthogonal db10 wavelet.

    fBm = wfbm(H,L,ns,w) returns the signal using ns reconstruction steps and the sufficiently regular orthogonal wavelet w.

    fBm = wfbm(H,L,w,ns) is equivalent to fBm = wfbm(H,L,ns,w).

    fBm = wfbm(H,L,'plot') generates and plots the fBm signal. The following syntaxes also generate and plot the signal.

    • fBm = wfbm(H,L,'plot',w)

    • fBm = wfbm(H,L,'plot',ns)

    • fBm = wfbm(H,L,'plot',w,ns)

    • fBm = wfbm(H,L,'plot',ns,w)


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    According to the value of the Hurst parameter H, the fBm exhibits for H > 0.5, long-range dependence and for H < 0.5, short or intermediate dependence. This example shows each situation using the wfbm function, which generates a sample path of this process.

    For purposes of reproducibility, set the random seed to the default value. Generate a fractional Brownian motion signal of length 1000 with the Hurst parameter of 0.3. Plot the signal.

    rng default
    h = 0.3;
    l = 1000;
    fBm03 = wfbm(h,l,'plot');

    Now generate a fractional Brownian motion signal of length 1000 with the Hurst parameter of 0.7. The signal clearly exhibits a stronger low-frequency component and has, locally, less irregular behavior than fBm03.

    h = 0.7;
    l = 1000;
    fBm07 = wfbm(h,l,'plot');

    Input Arguments

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    Hurst parameter, specified as a positive scalar strictly less than 1.

    Example: fBm = wfbm(0.4,1000) generates a fractional Brownian motion of length L = 1000 with Hurst parameter H = 0.4.

    Data Types: double

    Signal length, specified as a positive integer strictly greater than 100.

    Example: fBm = wfbm(0.1,500) generates a fractional Brownian motion of length L = 500 with Hurst parameter H = 0.1.

    Data Types: double

    Number of reconstruction steps, specified as a positive integer greater than 1.

    Data Types: double

    Orthogonal wavelet recognized by wavemngr, specified as a character vector or string scalar.

    Output Arguments

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    Fractional Brownian motion signal, returned as a vector of length L.

    More About

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    Fractional Brownian Motion

    A fractional Brownian motion (fBm) is a continuous-time Gaussian process depending on the Hurst parameter 0 < H < 1. It generalizes the ordinary Brownian motion corresponding to H = 0.5 and whose derivative is the white noise. The fBm is self-similar in distribution and the variance of the increments is given by

    Var(fBm(t)-fBm(s)) = v |t-s|^(2H)

    where v is a positive constant.


    Starting from the expression of the fBm process as a fractional integral of the white noise process, the idea of the algorithm is to build a biorthogonal wavelet depending on a given orthogonal one and adapted to the parameter H.

    Then the generated sample path is obtained by the reconstruction using the new wavelet starting from a wavelet decomposition at a given level designed as follows: details coefficients are independent random Gaussian realizations and approximation coefficients come from a fractional ARIMA process.

    This method was first proposed by Meyer and Sellan and implementation issues were examined by Abry and Sellan [1].

    Nevertheless, the samples generated following this original scheme exhibit too many high-frequency components. To circumvent this undesirable behavior Bardet et al. [2] propose downsampling the obtained sample by a factor of 10.

    Two internal parameters delta = 10 (the downsampling factor) and a threshold prec = 1E-4, to evaluate series by truncated sums, can be modified by the user for extreme values of H.

    A complete overview of long-range dependence process generators is available in Bardet et al [2].


    [1] Abry, Patrice, and Fabrice Sellan. “The Wavelet-Based Synthesis for Fractional Brownian Motion Proposed by F. Sellan and Y. Meyer: Remarks and Fast Implementation.” Applied and Computational Harmonic Analysis 3, no. 4 (October 1996): 377–83.

    [2] Bardet, Jean-Marc, Gabriel Lang, Georges Oppenheim, Anne Philippe, Stilian Stoev, and Murad S. Taqqu. “Generators of Long-Range Dependent Processes: A Survey.” In Theory and Applications of Long-Range Dependence, edited by Paul Doukhan, Georges Oppenheim, and Murad S. Taqqu, 579–623. Boston: Birkhäuser, 2003.

    Version History

    Introduced before R2006a

    See Also