Skip to main content Accessibility help
×
  1. Home
  2. Books
  3. Wavelet Methods for Time Series Analysis
  • Cited by 1901
      • Show more authors
      • You may already have access via personal or institutional login
    • Select format
    • Publisher:
      Cambridge University Press
      Publication date:
      05 December 2013
      24 July 2000
      ISBN:
      9780511841040
      9780521685085
      DOI:
      https://doi.org/10.1017/CBO9780511841040
      Dimensions:
      Weight & Pages:
      Dimensions:
      (253 x 177 mm)
      Weight & Pages:
      1.066kg, 622 Pages
      • Subjects:
      Communications and Signal Processing, Engineering, General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
      Series:
      Cambridge Series in Statistical and Probabilistic Mathematics (4)
    You may already have access via personal or institutional login
    • Selected: Digital
    Add to cart View cart Buy from Cambridge.org
    Subjects:
    Communications and Signal Processing, Engineering, General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
    Series:
    Cambridge Series in Statistical and Probabilistic Mathematics (4)
    Information

    Book description

    This introduction to wavelet analysis 'from the ground level and up', and to wavelet-based statistical analysis of time series focuses on practical discrete time techniques, with detailed descriptions of the theory and algorithms needed to understand and implement the discrete wavelet transforms. Numerous examples illustrate the techniques on actual time series. The many embedded exercises - with complete solutions provided in the Appendix - allow readers to use the book for self-guided study. Additional exercises can be used in a classroom setting. A Web site offers access to the time series and wavelets used in the book, as well as information on accessing software in S-Plus and other languages. Students and researchers wishing to use wavelet methods to analyze time series will find this book essential.

    Reviews

    ‘In my opinion the book by Percival and Walden should be available in every university library, and every time-series analyst must read this book for an alternative (to Fourier) set of techniques.’

    T. Subba Rao Source: Publication of the International Statistical Institute

    ‘… would be an ideal text for a statistics doctoral student who is new to the field of wavelets … the content, lay-out and consistency of the text mean that it should also be a valuable reference resource for the wavelet researcher.’

    Tim Downie Source: The Statistician

    ‘The authors … provide considerable background material, tell their story from scratch, proceed at a careful pace … and work out detailed applications … Recommended.’

    Source: Choice

    Refine List

    Actions for selected content:

    Select all | Deselect all
    • View selected items
    • Export citations
    • Download PDF (zip)
    • Save to Kindle
    • Save to Dropbox
    • Save to Google Drive

    Save Search

    You can save your searches here and later view and run them again in "My saved searches".

    Please provide a title, maximum of 40 characters.
    Cancel
    ×

    Contents

    Contents
    References

    Metrics

    Altmetric attention score

    Full text views

    Total number of HTML views: 0
    Total number of PDF views: 0 *
    Loading metrics...

    Book summary page views

    Total views: 0 *
    Loading metrics...

    * Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

    Usage data cannot currently be displayed.

    Accessibility standard: Unknown

    Why this information is here

    This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

    Accessibility Information

    Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.