Applied Neuro-Fuzzy using Support Vector Approximation for Stock Prediction

In general case, stock pricing pattern is similar to a noisy pattern with a slow changing curve. The global prediction techniques such as support vector (SV) show good enveloped prediction patterns but they tend to delay the prediction. Fuzzy methods have better local optimizing and show significant within training sets. Unfortunately, these sometimes give the surface oscillation effect at the output. Combining our previous prediction models, output component base (OCB) and output-input iteration (OII), results in significant compromise for stock prediction.