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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.
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