Hybrid deep learning approach for financial time series classification

Abstract

This paper proposes a combined approach of two machine learning techniques for financial time series classification. Boltzmann Restricted Machines (RBM) were used as the latent features extractor and Support Vector Machines (SVM) as the classifier. Tests were performed with real data of five assets from Brazilian Stock Market. The results of the combined RBM + SVM techniques showed better performance when compared to the isolated SVM, which suggests that the proposed approach can be suitable for the considered application.