  In this article, we propose a combination of an noise-reduction algorithm
based on Singular Spectrum Analysis (SSA) and a standard feedforward neural
prediction model. Basically, the proposed algorithm consists of two different
steps: data preprocessing based on the SSA filtering method and step-by-step
training procedure in which we use a simple feedforward multilayer neural
network with backpropagation learning. The proposed noise-reduction procedure
successfully removes most of the noise. That increases long-term predictability
of the processed dataset comparison with the raw dataset. The method was
applied to predict the International sunspot number RZ time series. The results
show that our combined technique has better performances than those offered by
the same network directly applied to raw dataset.
