Skip to content
Register Sign in Wishlist

Stochastic Analysis of Scaling Time Series
From Turbulence Theory to Applications

$74.99 (P)

  • Date Published: March 2016
  • availability: In stock
  • format: Hardback
  • isbn: 9781107067615

$ 74.99 (P)
Hardback

Add to cart Add to wishlist

Other available formats:
eBook


Looking for an examination copy?

If you are interested in the title for your course we can consider offering an examination copy. To register your interest please contact collegesales@cambridge.org providing details of the course you are teaching.

Description
Product filter button
Description
Contents
Resources
Courses
About the Authors
  • 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.

    • This book is the culmination of the authors' research over recent years, in which they have developed several new methods to deal with nonlinear and scaling time series
    • Applicable to many different fields of the natural sciences
    • MATLAB codes and experimental data are provided online, allowing the reader to reproduce the examples in the book and apply these methods to their own data
    Read more

    Customer reviews

    Not yet reviewed

    Be the first to review

    Review was not posted due to profanity

    ×

    , create a review

    (If you're not , sign out)

    Please enter the right captcha value
    Please enter a star rating.
    Your review must be a minimum of 12 words.

    How do you rate this item?

    ×

    Product details

    • Date Published: March 2016
    • format: Hardback
    • isbn: 9781107067615
    • length: 226 pages
    • dimensions: 253 x 179 x 15 mm
    • weight: 0.6kg
    • contains: 148 b/w illus.
    • availability: In stock
  • Table of Contents

    Preface
    1. Introduction: a multiscale and turbulent-like world
    2. Homogeneous turbulence and intermittency
    3. Scaling and intermittent stochastic processes
    4. New methodologies to deal with nonlinear and scaling time series
    5. Applications: case studies in turbulence
    6. Applications: case studies in ocean and atmospheric sciences
    References
    Index.

  • Resources for

    Stochastic Analysis of Scaling Time Series

    François G. Schmitt, Yongxiang Huang

    General Resources

    Find resources associated with this title

    Type Name Unlocked * Format Size

    Showing of

    Back to top

    This title is supported by one or more locked resources. Access to locked resources is granted exclusively by Cambridge University Press to instructors whose faculty status has been verified. To gain access to locked resources, instructors should sign in to or register for a Cambridge user account.

    Please use locked resources responsibly and exercise your professional discretion when choosing how you share these materials with your students. Other instructors may wish to use locked resources for assessment purposes and their usefulness is undermined when the source files (for example, solution manuals or test banks) are shared online or via social networks.

    Supplementary resources are subject to copyright. Instructors are permitted to view, print or download these resources for use in their teaching, but may not change them or use them for commercial gain.

    If you are having problems accessing these resources please contact lecturers@cambridge.org.

  • Authors

    François G. Schmitt, Centre National de la Recherche Scientifique (CNRS), Paris
    François G. Schmitt is Research Professor in the Laboratory of Oceanography and Geosciences at the Centre National de la Recherche Scientifique (CNRS), France. His research interests include turbulence and nonlinear variability in geophysics, marine turbulence, and multifractal analysis and modelling.

    Yongxiang Huang, Xiamen University
    Yongxiang Huang is Associate Professor in the State Key Laboratory of Marine Environmental Science at Xiamen University, China. He was awarded the 2013 Division Outstanding Young Scientists Award by the European Geosciences Union in Nonlinear Processes in Geosciences.

Sign In

Please sign in to access your account

Cancel

Not already registered? Create an account now. ×

Sorry, this resource is locked

Please register or sign in to request access. If you are having problems accessing these resources please email lecturers@cambridge.org

Register Sign in
Please note that this file is password protected. You will be asked to input your password on the next screen.

» Proceed

You are now leaving the Cambridge University Press website. Your eBook purchase and download will be completed by our partner www.ebooks.com. Please see the permission section of the www.ebooks.com catalogue page for details of the print & copy limits on our eBooks.

Continue ×

Continue ×

Continue ×

Find content that relates to you

Join us online

This site uses cookies to improve your experience. Read more Close

Are you sure you want to delete your account?

This cannot be undone.

Cancel

Thank you for your feedback which will help us improve our service.

If you requested a response, we will make sure to get back to you shortly.

×
Please fill in the required fields in your feedback submission.
×