  We study forecasting capabilities of the methods of Singular Spectrum
Analysis (SSA) and Local Approximation (LA). A practical implementation of
these methods to several time series is described. Details of the algorithms of
these methods are discussed. Advantages and disadvantages of SSA and LA are
given. On the basis of our results we generalize LA and SSA and propose a new
way for time series forecasting including strongly noisy signals. This allowed
us to extend the range of application of LA in comparison with the known
standard scheme. For the problem of forecasting, the accuracy enhancement of
the numerical computations is discussed.
