| CliftLRD-package {CliftLRD} | R Documentation |
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 <nunesrpackages@gmail.com>
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.