Lookup NU author(s): Dr Francesco Serinaldi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
The apparent ubiquity of binary random processes in physics and many other elds has attracted considerable attention from the modeling community. However, generation of binary sequences with prescribed autocorrelation is a challenging task owing to the discrete nature of the marginal distributions, which makes the application of classical spectral techniques problematic. We show that such methods can eectively be used if we focus on the parent continuous process of beta distributed transition probabilities rather than on the target binary process. This change of paradigm results in a simulation procedure eectively embedding spectrum-based iterative amplitude adjusted Fourier transform method devised for continuous processes. The proposed algorithm is fully general, requires minimal assumptions, and can easily simulate binary signals with power-law and exponentially decaying autocorrelation functions corresponding for instance to Hurst-Kolmogorov and Markov processes. An application to rainfall intermittency shows that the proposed algorithm can also simulate surrogate data preserving the empirical autocorrelation.
Author(s): Serinaldi F, Lombardo F
Publication type: Article
Publication status: Published
Journal: Physical Review E
Online publication date: 23/02/2017
Acceptance date: 24/01/2017
Date deposited: 24/01/2017
ISSN (print): 2470-0045
ISSN (electronic): 2470-0053
Publisher: American Physical Society
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