Author:
Benmehdi S.,Makarava N.,Benhamidouche N.,Holschneider M.
Abstract
Abstract. The aim of this paper is to estimate the Hurst parameter of Fractional Gaussian Noise (FGN) using Bayesian inference. We propose an estimation technique that takes into account the full correlation structure of this process. Instead of using the integrated time series and then applying an estimator for its Hurst exponent, we propose to use the noise signal directly. As an application we analyze the time series of the Nile River, where we find a posterior distribution which is compatible with previous findings. In addition, our technique provides natural error bars for the Hurst exponent.
Reference18 articles.
1. Abry, P. and Pipiras, V.: Wavelet-based synthesis of the Rosenblatt process, Signal Process., 86, 2326–2339, https://doi.org/10.1016/j.sigpro.2005.10.021, 2006.
2. Abry, P. and Veitch, D.: Wavelet Analysis of Long-Range-Dependent Traffic, IEEE T. Inform., 44, 2–15, 1998.
3. Dieker, T.: Simulation of Fractional Brownian motion, Ph.D. thesis, Twente University, 2004.
4. Geweke, K. and Porter-Hudak, S.: The estimation and application of long memory time series models, Time Ser. Anal., 4, 221–238, 1983.
5. Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J., Ivanov, P., Mark, R., Mietus, J., Moody, G., Peng, C., and Stanley, H.: Components of a New Research Resource for Complex Physiologic Signals, PhysioBank, PhysioToolkit, and Physionet, Circulation, 101(23), e215–e220, 2000.
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献