Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/3838
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dc.contributor.authorSezer, Ömer Berat-
dc.contributor.authorGüdelek, Mehmet Uğur-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.date.accessioned2020-10-22T16:40:33Z-
dc.date.available2020-10-22T16:40:33Z-
dc.date.issued2020-05
dc.identifier.citationSezer, O. B., Gudelek, M. U. and Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181.en_US
dc.identifier.issn1568-4946
dc.identifier.urihttps://hdl.handle.net/20.500.11851/3838-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1568494620301216?via%3Dihub-
dc.identifier.urihttps://ui.adsabs.harvard.edu/abs/2019arXiv191113288B/abstract-
dc.description.abstractFinancial time series forecasting is undoubtedly the top choice of computational intelligence for finance researchers in both academia and the finance industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers have created various models, and a vast number of studies have been published accordingly. As such, a significant number of surveys exist covering ML studies on financial time series forecasting. Lately, Deep Learning (DL) models have appeared within the field, with results that significantly outperform their traditional ML counterparts. Even though there is a growing interest in developing models for financial time series forecasting, there is a lack of review papers that solely focus on DL for finance. Hence, the motivation of this paper is to provide a comprehensive literature review of DL studies on financial time series forecasting implementation. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, and commodity forecasting, but we also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM). We also tried to envision the future of the field by highlighting its possible setbacks and opportunities for the benefit of interested researchers. (C) 2020 Elsevier B.V. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofApplied Soft Computing Journalen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectfinanceen_US
dc.subjectcomputational intelligenceen_US
dc.subjectmachine learningen_US
dc.subjecttime series forecastingen_US
dc.subjectCNNen_US
dc.subjectLSTMen_US
dc.subjectRNNen_US
dc.titleFinancial time series forecasting with deep learning : A systematic literature review: 2005-2019en_US
dc.typeReviewen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.volume90
dc.relation.tubitakinfo:eu-repo/grantAgreement/TÜBİTAK/EEEAG/215E248en_US
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000529902300014en_US
dc.identifier.scopus2-s2.0-85079891275en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1016/j.asoc.2020.106181-
dc.relation.publicationcategoryDiğeren_US
item.openairetypeReview-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.grantfulltextnone-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering
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