Genetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorMato Grosso State University-
Autor(es): dc.creatorContreras, Rodrigo Colnago-
Autor(es): dc.creatorXavier da Silva, Vitor Trevelin-
Autor(es): dc.creatorXavier da Silva, Igor Trevelin-
Autor(es): dc.creatorViana, Monique Simplicio-
Autor(es): dc.creatorSantos, Francisco Lledo dos-
Autor(es): dc.creatorZanin, Rodrigo Bruno-
Autor(es): dc.creatorMartins, Erico Fernandes Oliveira-
Autor(es): dc.creatorGuido, Rodrigo Capobianco-
Data de aceite: dc.date.accessioned2025-08-21T17:49:43Z-
Data de disponibilização: dc.date.available2025-08-21T17:49:43Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/e26030177-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/298194-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/298194-
Descrição: dc.descriptionSince financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested.-
Descrição: dc.descriptionDepartment of Computer Science and Statistics Institute of Biosciences Letters and Exact Sciences São Paulo State University (UNESP), SP-
Descrição: dc.descriptionDepartment of Applied Mathematics and Statistics Institute of Mathematical and Computer Sciences University of São Paulo, SP-
Descrição: dc.descriptionDepartment of Computing Federal University of São Carlos, SP-
Descrição: dc.descriptionFaculty of Architecture and Engineering Mato Grosso State University, MT-
Descrição: dc.descriptionDepartment of Computer Science and Statistics Institute of Biosciences Letters and Exact Sciences São Paulo State University (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationEntropy-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBitcoin-
Palavras-chave: dc.subjectfeature selection-
Palavras-chave: dc.subjectforecasting-
Palavras-chave: dc.subjectgenetic algorithm-
Palavras-chave: dc.subjectmachine learning-
Palavras-chave: dc.subjecttime series-
Título: dc.titleGenetic Algorithm for Feature Selection Applied to Financial Time Series Monotonicity Prediction: Experimental Cases in Cryptocurrencies and Brazilian Assets-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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