Wavelet Methods for Time Series Analysis

Cambridge Series in Statistical and Probabilistic Mathematics #4

by Donald B. Percival, Andrew T. Walden

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

Genres: Science, Academic

594 pages, Paperback

First published July 24, 2000

Reviews

[Elí Cuéllar · Rating 5 out of 5]

This book opened my third eye: where all I could see was Time and Frequency, I now bask in the clairvoyance of Scale.

So I know people treat math books as textbooks and not real books. Fuck those people. The wavelet transform is one of the most beautiful, elegant and powerful ideas that humankind has come up with.

If you think the Fourier transform is great, this shit is going to blow your mind.

a) A decomposition that retains the entirety of the information in the original signal.
b) A decomposition whose components are non-autocorrelated even for highly autocorrelated data.
c) Computationally no more complex than the FFT.

If you do any kind of work with signals or timeseries and you're not on this shit yet, man, you're missing out on 1/3th of the picture.