Stochastic Analysis of Scaling Time Series

From Turbulence Theory to Applications

Nonfiction, Science & Nature, Science, Earth Sciences, Nature
Cover of the book Stochastic Analysis of Scaling Time Series by François G. Schmitt, Yongxiang Huang, Cambridge University Press
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Author: François G. Schmitt, Yongxiang Huang ISBN: 9781316461334
Publisher: Cambridge University Press Publication: January 7, 2016
Imprint: Cambridge University Press Language: English
Author: François G. Schmitt, Yongxiang Huang
ISBN: 9781316461334
Publisher: Cambridge University Press
Publication: January 7, 2016
Imprint: Cambridge University Press
Language: English

Multi-scale systems, involving complex interacting processes that occur over a range of temporal and spatial scales, are present in a broad range of disciplines. Several methodologies exist to retrieve this multi-scale information from a given time series; however, each method has its own limitations. This book presents the mathematical theory behind the stochastic analysis of scaling time series, including a general historical introduction to the problem of intermittency in turbulence, as well as how to implement this analysis for a range of different applications. Covering a variety of statistical methods, such as Fourier analysis and wavelet transforms, it provides readers with a thorough understanding of the techniques and when to apply them. New techniques to analyse stochastic processes, including empirical mode decomposition, are also explored. Case studies, in turbulence and ocean sciences, are used to demonstrate how these statistical methods can be applied in practice, for students and researchers.

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Multi-scale systems, involving complex interacting processes that occur over a range of temporal and spatial scales, are present in a broad range of disciplines. Several methodologies exist to retrieve this multi-scale information from a given time series; however, each method has its own limitations. This book presents the mathematical theory behind the stochastic analysis of scaling time series, including a general historical introduction to the problem of intermittency in turbulence, as well as how to implement this analysis for a range of different applications. Covering a variety of statistical methods, such as Fourier analysis and wavelet transforms, it provides readers with a thorough understanding of the techniques and when to apply them. New techniques to analyse stochastic processes, including empirical mode decomposition, are also explored. Case studies, in turbulence and ocean sciences, are used to demonstrate how these statistical methods can be applied in practice, for students and researchers.

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