  Singular spectrum analysis (SSA) as a nonparametric tool for decomposition of
an observed time series into sum of interpretable components such as trend,
oscillations and noise is considered. The separability of these series
components by SSA means the possibility of such decomposition. Two variations
of SSA, which weaken the separability conditions, are proposed. Both proposed
approaches consider inner products corresponding to oblique coordinate systems
instead of the conventional Euclidean inner product. One of the approaches
performs iterations to obtain separating inner products. The other method
changes contributions of the components by involving the series derivative to
avoid component mixing. Performance of the suggested methods is demonstrated on
simulated and real-life data.
