Exploiting Visual Features in Financial Time Series Prediction

dc.authorid Erkan, Turan Erman/0000-0002-0078-711X
dc.authorid Erkan, Turan Erman/0000-0002-0078-711X
dc.authorscopusid 16417519900
dc.authorscopusid 23974132900
dc.authorwosid Erkan, Turan Erman/A-7318-2018
dc.authorwosid Erkan, Turan Erman/HLP-6760-2023
dc.contributor.author Karacor, Adil Gursel
dc.contributor.author Erkan, Turan Erman
dc.contributor.other Industrial Engineering
dc.date.accessioned 2024-07-05T15:38:23Z
dc.date.available 2024-07-05T15:38:23Z
dc.date.issued 2020
dc.department Atılım University en_US
dc.department-temp [Karacor, Adil Gursel] Atilim Univ, Modeling & Design Engn Syst, Ankara, Turkey; [Erkan, Turan Erman] Atilim Univ, Ind Engn Dept, Ankara, Turkey en_US
dc.description Erkan, Turan Erman/0000-0002-0078-711X; Erkan, Turan Erman/0000-0002-0078-711X en_US
dc.description.abstract The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name 'Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance. en_US
dc.identifier.citationcount 1
dc.identifier.doi 10.4018/IJCINI.2020040104
dc.identifier.endpage 76 en_US
dc.identifier.issn 1557-3958
dc.identifier.issn 1557-3966
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-85100707956
dc.identifier.scopusquality Q4
dc.identifier.startpage 61 en_US
dc.identifier.uri https://doi.org/10.4018/IJCINI.2020040104
dc.identifier.uri https://hdl.handle.net/20.500.14411/3106
dc.identifier.volume 14 en_US
dc.identifier.wos WOS:000518411900004
dc.institutionauthor Erkan, Turan Erman
dc.language.iso en en_US
dc.publisher Igi Global en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 4
dc.subject Artificial Neural Networks en_US
dc.subject Extreme Gradient Boosting en_US
dc.subject Forex en_US
dc.subject Machine Learning en_US
dc.subject Machine Vision en_US
dc.subject Predictability en_US
dc.subject Quantitative Analysis en_US
dc.subject Support Vector Machine en_US
dc.title Exploiting Visual Features in Financial Time Series Prediction en_US
dc.type Article en_US
dc.wos.citedbyCount 2
dspace.entity.type Publication
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relation.isOrgUnitOfPublication.latestForDiscovery 12c9377e-b7fe-4600-8326-f3613a05653d

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