A two-stage architecture for stock price forecasting by combining SOM and fuzzy-SVM
This is an incremental improvement for financial forecasting applications.
The paper tackled stock price forecasting by proposing a two-stage model combining Self-Organizing Map (SOM) and fuzzy-SVM, which achieved performance improvements compared to other models on four datasets.
This paper proposed a model to predict the stock price based on combining Self-Organizing Map (SOM) and fuzzy-Support Vector Machines (f-SVM). Extraction of fuzzy rules from raw data based on the combining of statistical machine learning models is base of this proposed approach. In the proposed model, SOM is used as a clustering algorithm to partition the whole input space into the several disjoint regions. For each partition, a set of fuzzy rules is extracted based on a f-SVM combining model. Then fuzzy rules sets are used to predict the test data using fuzzy inference algorithms. The performance of the proposed approach is compared with other models using four data sets