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

Training Algorithm for Neuro-Fuzzy Network Based on Singular Spectrum Analysis