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

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/Hfrombeta.R

Description

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

Usage

1
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

1
Hfrombeta(0.8,model="FGN")

liftLRD documentation built on Sept. 12, 2017, 1:04 a.m.