In the present paper we investigate methods related to both the Singular
Spectrum Analysis (SSA) and subspace-based methods. We describe common and
specific features of these methods and consider different kinds of problems
solved by them such as signal reconstruction, forecasting and parameter
estimation. General recommendations on the choice of parameters to obtain
minimal errors are provided. We demonstrate that the optimal choice depends on
the particular problem.