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.
