Hfrombeta: Compute Hurst exponent from wavelet scale - energy regression...

In nunesmatt/liftLRD: Wavelet Lifting Estimators of the Hurst Exponent for Regularly and Irregularly Sampled Time Series

Description

Uses the slope of the relationship between wavelet scale and wavelet energy to compute an estimate of the Hurst exponent

Usage

Hfrombeta(beta, model = c("FBM","FGN","ID"))

Arguments

beta	The estimated slope of the relationship between wavelet scale and energy.
model	The assumed long-range dependence model for the time series under analysis.

Details

There is a theoretical linear relationship growth in the (log) wavelet energy for increasing wavelet scale. This corresponds to the decay in the autocorrelation of a (long range dependent) time series being analysed, and therefore the Hurst exponent, H. The specific relation to H is dependent to the assumed model; in particular for a Fractional Brownian motion, the relationship between H and the slope is H = abs(beta - 1)/2, whereas for Fractional Gaussian noise or dth order Fractional differenced series, the relationship is H = (beta+1)/2.

Value

H	The Hurst exponent, computed for a specific beta and underlying model.

Author(s)

Matt Nunes

References

Knight, M. I, Nason, G. P. and Nunes, M. A. (2017) A wavelet lifting approach to long-memory estimation. Stat. Comput. 27 (6), 1453–1471. DOI 10.1007/s11222-016-9698-2.

Beran, J. et al. (2013) Long-Memory Processes. Springer.

See Also

liftHurst

Examples

Hfrombeta(0.8,model="FGN")

nunesmatt/liftLRD documentation built on May 15, 2019, 4:16 p.m.