Books  Data Analysis & Modelling  Data Analysis & Statistics 

Wavelet Methods for Time Series Analysis

By: Donald B Percival and Andrew T Walden

620 pages, no illustrations

Cambridge University Press

Paperback | Jun 2006 [2006 reprint] | #159623 | ISBN: 0521685087
Availability: Usually dispatched within 6 days Details
NHBS Price: £49.99 $66/€56 approx
Hardback | Oct 2000 | #159624 | ISBN: 0521640687
Out of Print Details

About this book

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.

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


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.

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