Wavelet Methods for Time Series Analysis

✍ Scribed by Donald B. Percival, Andrew T. Walden

• Publisher: Cambridge University Press
• Year: 2013
• Tongue: English
• Leaves: 613
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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Wavelet Methods for Time Series Analysis

Conventions and Notation

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