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Wavelet Methods for Time Series Analysis

Wavelet Methods for Time Series Analysis

$77.00 (P)

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Date Published: February 2006
  • availability: Available
  • format: Paperback
  • isbn: 9780521685085

$ 77.00 (P)
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About the Authors
  • The analysis of time series data is essential to many areas of science, engineering, finance and economics. 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. Author resource page: http://faculty.washington.edu/dbp/wmtsa.html

    • Covers both theory and practice
    • Numerous exercises and examples, detailed solutions provided
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    Reviews & endorsements

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

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    Product details

    • Date Published: February 2006
    • format: Paperback
    • isbn: 9780521685085
    • length: 622 pages
    • dimensions: 244 x 170 x 32 mm
    • weight: 0.98kg
    • contains: 24 tables 119 exercises
    • availability: Available
  • Table of Contents

    1. Introduction to wavelets
    2. Review of Fourier theory and filters
    3. Orthonormal transforms of time series
    4. The discrete wavelet transform
    5. The maximal overlap discrete wavelet transform
    6. The discrete wavelet packet transform
    7. Random variables and stochastic processes
    8. The wavelet variance
    9. Analysis and synthesis of long memory processes
    10. Wavelet-based signal estimation
    11. Wavelet analysis of finite energy signals
    Appendix. Answers to embedded exercises
    References
    Author index
    Subject index.

  • Authors

    Donald B. Percival, University of Washington

    Andrew T. Walden, Imperial College of Science, Technology and Medicine, London

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