  Singular spectrum analysis (SSA), starting from the second half of the XX
century, has been a rapidly developing method of time series analysis. Since it
can be called principal component analysis for time series, SSA will definitely
be a standard method in time series analysis and signal processing in the
future. Moreover, the problems solved by SSA are considerably wider than that
for principal component analysis. In particular, the problems of frequency
estimation, forecasting and missing values imputation can be solved within the
framework of SSA. The idea of SSA came from different scientific communities,
such as that of researchers in time series analysis (Karhunen-Loeve
decomposition), signal processing (low-rank approximation and frequency
estimation) and multivariate data analysis (principal component analysis).
Also, depending on the area of applications, different viewpoints on the same
algorithms, choice of parameters, and methodology as a whole are considered.
Thus, the aim of the paper is to describe and compare different viewpoints on
SSA and its modifications and extensions to give people from different
scientific communities the possibility to be aware of potentially new aspects
of the method.
