  In the present paper we investigate methods related to both the Singular
Spectrum Analysis (SSA) and subspace-based methods in signal processing. 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. For the basic model `signal + residual' we
show that the error behavior depends on the type of residuals, deterministic or
stochastic, and whether the noise is white or red. The structure of errors and
the convergence rate are also discussed. The analysis is based on known
theoretical results and extensive computer simulations.
