This book provides a broad introduction to computational aspects of Singular Spectrum Analysis
(SSA) which is a non-parametric technique and requires no prior assumptions such as
stationarity normality or linearity of the series. This book is unique as it not only details
the theoretical aspects underlying SSA but also provides a comprehensive guide enabling the
user to apply the theory in practice using the R software. Further it provides the user with
step- by- step coding and guidance for the practical application of the SSA technique to
analyze their time series databases using R. The first two chapters present basic notions of
univariate and multivariate SSA and their implementations in R environment. The next chapters
discuss the applications of SSA to change point detection missing-data imputation smoothing
and filtering. This book is appropriate for researchers upper level students (masters level
and beyond) and practitioners wishing to revive their knowledge of times series analysis or to
quickly learn about the main mechanisms of SSA.
