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This paper proposed a model to predict the stock price based on combiningSelf-Organizing Map (SOM) and fuzzy-Support Vector Machines (f-SVM). Extractionof fuzzy rules from raw data based on the combining of statistical machinelearning models is base of this proposed approach. In the proposed model, SOMis used as a clustering algorithm to partition the whole input space into theseveral disjoint regions. For each partition, a set of fuzzy rules is extractedbased on a f-SVM combining model. Then fuzzy rules sets are used to predict thetest data using fuzzy inference algorithms. The performance of the proposedapproach is compared with other models using four data sets

A two-stage architecture for stock price forecasting by combining SOM and fuzzy-SVM