The book "Singular Spectrum Analysis with R" (Use R!) (2018, in English) is devoted to description of methods, algorithms and implementation in R
for SSA, MSSA, 2D-SSA applied to time series, collections of time series and images, respectively.
SSA extensions and modifications are structured and described in a unified manner to show their commonality and difference.
The book contains description of the R package Rssa with many real-life examples,
which are implemented by means of this package. This can be extremely useful for those who are going to implement the data processing in R with the help of singular spectrum analysis.
The companion site contains useful information related to the book.
The book "Singular Spectrum Analysis for Time Series" (2013, in English) is devoted to the methodology of SSA and shows how to use SSA both safely and with maximum effect. Potential readers of the book include: professional statisticians and econometricians, specialists in any discipline in which problems of time series analysis and forecasting occur, specialists in signal processing and those needed to extract signals from noisy data, and students taking courses on applied time series analysis.
The book "Analysis of time series structure: SSA and related techniques" (2001, in English) provides a careful, lucid description of the 'Caterpillar'-SSA general theory and methodology. Based on the authors' original work and filled with applications illustrated with real data sets, this book offers you the opportunity to obtain a working knowledge of why, when, and how SSA works. It builds a strong foundation for successfully using the technique in applications. All examples of the book are obtained with the help of the CaterpillarSSA v3.10 software.
The book "Principal Components of Time Series: the Caterpillar Method" (1997, in Russian) forms the basis of the Russian part of this site. The collection of papers is devoted to the unified theme: methodology (with many examples), theoretical ground and practicality of the method.
Special Issue "Theory and Practice in Singular Spectrum Analysis of Time Series"
(draft versions are published in Arxiv)
of the journal Statistics and Its Interface, 2010, v. 3, No. 3:
Computation- and Space-Efficient Implementation of SSA. P.357-368
Perturbation expansions of signal subspaces for long signals. P.297-319
On signal and extraneous roots in Singular Spectrum Analysis. P.281-295
On the choice of parameters in Singular Spectrum Analysis and related subspace-based methods. P.259-279