Spectral Analysis for Univariate Time Series

Focuses on practical application of spectral analysis of time series, with examples from environmental, engineering and physical sciences.

Donald B. Percival (Author), Andrew T. Walden (Author)

9781107028142, Cambridge University Press

Hardback, published 19 March 2020

780 pages, 695 b/w illus.
25.9 x 18.2 x 4.3 cm, 1.44 kg

'The excellent new textbook by Percival and Walden is an important source of information for anyone interested in time series analysis. Theoretical rigour combined with practical analysis of interesting real world data gives the reader a pedagogical journey into the world of spectral analysis and time series analysis. Highly recommended!' Alfred Hanssen, Universitetet i Tromsø – Norges arktiske universitet

Spectral analysis is widely used to interpret time series collected in diverse areas. This book covers the statistical theory behind spectral analysis and provides data analysts with the tools needed to transition theory into practice. Actual time series from oceanography, metrology, atmospheric science and other areas are used in running examples throughout, to allow clear comparison of how the various methods address questions of interest. All major nonparametric and parametric spectral analysis techniques are discussed, with emphasis on the multitaper method, both in its original formulation involving Slepian tapers and in a popular alternative using sinusoidal tapers. The authors take a unified approach to quantifying the bandwidth of different nonparametric spectral estimates. An extensive set of exercises allows readers to test their understanding of theory and practical analysis. The time series used as examples and R language code for recreating the analyses of the series are available from the book's website.

1. Introduction to spectral analysis
2. Stationary stochastic processes
3. Deterministic spectral analysis
4. Foundations for stochastic spectral analysis
5. Linear time-invariant filters
6. Periodogram and other direct spectral estimators
7. Lag window estimators
8. Combining direct spectral estimators
9. Parametric spectral estimators
10. Harmonic analysis
11. Simulation of time series.

Subject Areas: Signal processing [UYS], Mathematical theory of computation [UYA], Geographical information systems [GIS & remote sensing RGW], Physical geography & topography [RGB], Statistical physics [PHS], Probability & statistics [PBT]