Training Algorithm for Neuro-Fuzzy Network Based on Singular Spectrum Analysis
This is an incremental improvement for researchers in time series forecasting, specifically in solar activity prediction.
The authors tackled the problem of improving time series prediction by combining Singular Spectrum Analysis (SSA) for noise reduction with a feedforward neural network, resulting in better performance for predicting the International sunspot number RZ time series compared to using the raw dataset directly.
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.