GoldMiner: A genetic programming based algorithm applied to Brazilian Stock Market

Pimenta, Alexandre; Guimarães, Frederico Gadelha; Carrano, Eduardo G.; Nametala, Ciniro Aparecido Leite; Takahashi, Ricardo H. C.

doi:10.1109/cidm.2014.7008695

Cited by 6 publications

(1 citation statement)

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“…Many researchers Meesad and Rasel (2013); Nametala et al (2016); Persio and Honchar (2016); Patel et al (2015); Pimenta et al (2014) have worked towards the improvement of already existing predictors. Many of these studies are based on recent machine learning techniques, such as Support Vector Machines (SVM) (Vapnik;1995), Arti cial Neural Networks (ANNs) (McCulloch and Pitts;1943) and Genetic Programming (GP) (Koza;1992).…”

Section: Introductionmentioning

confidence: 99%

Hybrid deep learning approach for financial time series classification

Assis¹,

Machado²,

Pereira³

et al. 2018

RBCA

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This paper proposes a combined approach of two machine learning techniques for nancial time series classi cation. Boltzmann Restricted Machines (RBM) were used as the latent features extractor and Support Vector Machines (SVM) as the classi er. Tests were performed with real data of ve 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. ResumoEste trabalho propõe uma abordagem combinada de duas técnicas de aprendizado de máquina para classi cação de séries temporais nanceiras. Foram utilizadas Máquinas de Boltzmann Restritas (RBM) como o extrator de características latentes e Máquinas de Vetores de Suporte (SVM) como o classi cador. Cinco conjuntos de dados reais de ativos ou ações da BM&FBOVESPA foram usados para validação da abordagem. Em geral, as técnicas RBM + SVM combinadas apresentaram resultados superiores ao SVM isolado, con rmando o potencial da abordagem proposta.

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