This comprehensive and richly illustrated volume provides up-to-date
material on Singular Spectrum Analysis (SSA). SSA is a well-known
methodology for the analysis and forecasting of time series. Since quite
recently, SSA is also being used to analyze digital images and other
objects that are not necessarily of planar or rectangular form and may
contain gaps. SSA is multi-purpose and naturally combines both
model-free and parametric techniques, which makes it a very special and
attractive methodology for solving a wide range of problems arising in
diverse areas, most notably those associated with time series and
digital images. An effective, comfortable and accessible implementation
of SSA is provided by the R-package Rssa, which is available from CRAN
and reviewed in this book.
Written by prominent statisticians who have extensive experience with
SSA, the book (a) presents the up-to-date SSA methodology, including
multidimensional extensions, in language accessible to a large circle of
users, (b) combines different versions of SSA into a single tool, (c)
shows the diverse tasks that SSA can be used for, (d) formally describes
the main SSA methods and algorithms, and (e) provides tutorials on the
Rssa package and the use of SSA.
The book offers a valuable resource for a very wide readership,
including professional statisticians, specialists in signal and image
processing, as well as specialists in numerous applied disciplines
interested in using statistical methods for time series analysis,
forecasting, signal and image processing. The book is written on a level
accessible to a broad audience and includes a wealth of examples; hence
it can also be used as a textbook for undergraduate and postgraduate
courses on time series analysis and signal processing.
