Restricted Boltzmann Machines For The Prediction Of Trends In Financial Time Series
2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)(2018)
摘要
Nowadays, it is possible to note many machine learning techniques being applied to predict financial time series. However, recent studies indicate that the performance of such techniques can be strongly affected by data representation. In this manuscript we propose a combination of two machine learning algorithms to detect trends in stock market prices. In this approach, Boltzmann Restricted Machines are used as the latent feature extractor and Support Vector Machines work as the classifier. We performed tests with real data of five assets from the Brazilian Stock Market, BM&FBOVESPA. The results obtained with the proposed combination were better when compared to those ones reached by Support Vector Machines only. This suggests that the proposed approach can be suitable for the considered application.
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关键词
Restricted Boltzmann Machines, Machine Learning, Stock Market
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