Singular Spectrum Analysis with R

by Nina Golyandina, Anton Korobeynikov, Anatoly Zhigljavsky
Springer, 2018. Front Matter (includes Preface) and Back Matter

Singular Spectrum Analysis (SSA) is a well-known methodology of analysis and forecasting of time series and, since quite recently, of digital images and other objects which are not necessarily of planar or rectangular shape and may contain gaps.

This book has the following goals:

• to present the up-to-date SSA methodology, including multidimensional extensions, in the form accessible to a very wide circle of users,
• to interconnect a variety of versions of SSA into a single tool,
• to show the diverse tasks that SSA can be used for,
• to formally describe the main SSA methods and algorithms, and
• to make a tutorial on the "Rssa" package.