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Improved Preservation of Autocorrelative Structure in Surrogate Data Using an Initial Wavelet Step : Volume 15, Issue 3 (02/06/2008)

By Keylock, C. J.

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Book Id: WPLBN0003980106
Format Type: PDF Article :
File Size: Pages 10
Reproduction Date: 2015

Title: Improved Preservation of Autocorrelative Structure in Surrogate Data Using an Initial Wavelet Step : Volume 15, Issue 3 (02/06/2008)  
Author: Keylock, C. J.
Volume: Vol. 15, Issue 3
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: copernicus


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Keylock, C. J. (2008). Improved Preservation of Autocorrelative Structure in Surrogate Data Using an Initial Wavelet Step : Volume 15, Issue 3 (02/06/2008). Retrieved from

Description: School of Geography, University of Leeds, Leeds, UK. Surrogate data generation algorithms are useful for hypothesis testing or for generating realisations of a process for data extension or modelling purposes. This paper tests a well known surrogate data generation method against a stochastic and also a hybrid wavelet-Fourier transform variant of the original algorithm. The data used for testing vary in their persistence and intermittency, and include synthetic and actual data. The hybrid wavelet-Fourier algorithm outperforms the others in its ability to match the autocorrelation function of the data, although the advantages decrease for high intermittencies and when attention is only directed towards the early part of the autocorrelation function. The improved performance is attributed to the wavelet step of the algorithm.

Improved preservation of autocorrelative structure in surrogate data using an initial wavelet step

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