Fractional Brownian motion realizations.
- Exact methods for simulating fractional Brownian motion (fBm) or fractional Gaussian noise (fGn) in python.
- Approximate simulation of multifractional Brownian motion (mBm) or multifractional Gaussian noise (mGn).
The fbm package is available on PyPI and can be installed via pip:
pip install fbm
fractional Brownian motion
Fractional Brownian motion can be generated via either Hosking’s method, the Cholesky method, or the Davies-Harte method. All three methods are theoretically exact in generating a discretely sampled fBm/fGn.
from fbm import FBM f = FBM(n=1024, hurst=0.75, length=1, method='daviesharte') # or f = FBM(1024, 0.75) # Generate a fBm realization fbm_sample = f.fbm() # Generate a fGn realization fgn_sample = f.fgn() # Get the times associated with the fBm t_values = f.times()
where n is the number of equispaced increments desired for a fBm with Hurst parameter hurst on the interval [0, length]. Method can be either 'hosking', 'cholesky', or 'daviesharte'. The fbm() method returns a length n+1 array of discrete values for the fBm (includes 0). The fgn() method returns a length n array of fBm increments, or fGn. The times() method returns a length n+1 array of times corresponding to the fBm realizations.
The n and hurst parameters are required. The length parameter defaults to 1 and method defaults to 'daviesharte'.
For simulating multiple realizations use the FBM class provided as above. Some intermediate values are cached for repeated simulation.
For one-off samples of fBm or fGn there are separate functions available:
from fbm import fbm, fgn, times # Generate a fBm realization fbm_sample = fbm(n=1024, hurst=0.75, length=1, method='daviesharte') # Generate a fGn realization fgn_sample = fgn(n=1024, hurst=0.75, length=1, method='daviesharte') # Get the times associated with the fBm t_values = times(n=1024, length=1)
For fastest performance use the Davies and Harte method. Note that the Davies and Harte method can fail if the Hurst parameter hurst is close to 1 and there are a small amount of increments n. If this occurs, a warning is printed to the console and it will fallback to using Hosking’s method to generate the realization. See page 412 of the following paper for a more detailed explanation:
- Wood, Andrew TA, and Grace Chan. “Simulation of stationary Gaussian processes in [0, 1] d.” Journal of computational and graphical statistics 3, no. 4 (1994): 409-432.
- Hosking, Jonathan RM. “Modeling persistence in hydrological time series using fractional differencing.” Water resources research 20, no. 12 (1984): 1898-1908.
- Asmussen, Søren. Stochastic simulation with a view towards stochastic processes. University of Aarhus. Centre for Mathematical Physics and Stochastics (MaPhySto)[MPS], 1998.
Davies Harte method:
- Davies, Robert B., and D. S. Harte. “Tests for Hurst effect.” Biometrika 74, no. 1 (1987): 95-101.
multifractional Brownian motion
This package supports approximate generation of multifractional Brownian motion. The current method uses the Riemann–Liouville fractional integral representation of mBm.
import math from fbm import MBM # Example Hurst function with respect to time. def h(t): return 0.25 * math.sin(20*t) + 0.5 m = MBM(n=1024, hurst=h, length=1, method='riemannliouville') # or m = MBM(1024, h) # Generate a mBm realization mbm_sample = m.mbm() # Generate a mGn realization mgn_sample = m.mgn() # Get the times associated with the mBm t_values = m.times()
The hurst argument here should be a callable that accepts one argument and returns a float in (0, 1).
For one-off samples of mBm or mGn there are separate functions available:
from fbm import mbm, mgn, times # Define a hurst function def h(t): return 0.75 - 0.5 * t # Generate a mbm realization mbm_sample = mbm(n=1024, hurst=h, length=1, method='riemannliouville') # Generate a fGn realization mgn_sample = mgn(n=1024, hurst=h, length=1, method='riemannliouville') # Get the times associated with the mBm t_values = times(n=1024, length=1)
Riemann-Liouville representation method:
Approximate method originally proposed for fBm in
- Rambaldi, Sandro, and Ombretta Pinazza. “An accurate fractional Brownian motion generator.” Physica A: Statistical Mechanics and its Applications 208, no. 1 (1994): 21-30.
Adapted to approximate mBm in
- Muniandy, S. V., and S. C. Lim. “Modeling of locally self-similar processes using multifractional Brownian motion of Riemann-Liouville type.” Physical Review E 63, no. 4 (2001): 046104.
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