Stochastic analysis of scaling time series: from turbulence theory to applications

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 limitation...

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Bibliographic Details
Main Authors: Schmitt, François G. (Author), Huang, Yongxiang 1982- (Author)
Format: Electronic eBook
Language:English
Published: Cambridge Cambridge University Press 2015
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Summary: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
Item Description:Title from publisher's bibliographic system (viewed on 12 Feb 2016)
Physical Description:1 Online-Ressource (226 Seiten)
ISBN:9781107705548
DOI:10.1017/CBO9781107705548

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