CliftLRD-package: Complex-Valued Wavelet Lifting Estimators of the Hurst...

In CliftLRD: Complex-Valued Wavelet Lifting Estimators of the Hurst Exponent for Irregularly Sampled Time Series

Description

Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales from complex-valued lifting schemes and wavelet energy.

Details

Package information:

Package:	CliftLRD
Type:	Package
Version:	0.1-1
Date:	2018-07-09
License:	GPL-2

This package exploits a complex-valued wavelet transform for irregularly spaced data to form wavelet-like scale-based energy measures for a time series. This is then used to estimate the Hurst exponent for real- and complex-valued time series. The main routines are

liftHurstC and liftHurstCC

Author(s)

Matt Nunes, Marina Knight

Maintainer: Matt Nunes <[e-mail]>

References

Knight, M. I, and Nunes, M. A. (2018) Long memory estimation for complex-valued time series. Stat. Comput. (to appear). Online First Article: DOI 10.1007/s11222-018-9820-8.

For related literature on the lifting methodology adopted in the technique, see

Hamilton, J., Nunes, M. A., Knight, M. I. and Fryzlewicz, P. (2017) Complex-valued lifting and applications. Technom.,60 (1), 48–60.

For more information on long-memory processes, see e.g.

Beran, J. et al. (2013) Long-memory processes. Springer.
Lilly, J. M., Sykulski, A. M., Early, J. J. and Olhede, S. C. (2017) Fractional Brownian motion, the Mat\'ern process, and stochastic modeling of turbulent dispersion. Nonlin. Proc. Geophys., 24, 481–514.

CliftLRD documentation built on May 1, 2019, 10:29 p.m.