Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10352
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKeskin, Mustafa Mert-
dc.contributor.authorİrim, Fatih-
dc.contributor.authorKaraahmetoglu, Oguzhan-
dc.contributor.authorKaya, Ersin-
dc.date.accessioned2023-04-16T10:01:16Z-
dc.date.available2023-04-16T10:01:16Z-
dc.date.issued2023-
dc.identifier.issn1863-1703-
dc.identifier.issn1863-1711-
dc.identifier.urihttps://doi.org/10.1007/s11760-022-02426-6-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10352-
dc.description.abstractIn this paper, we investigate the capability of modeling distant temporal interaction of Long Short-Term Memory (LSTM) and introduce a novel Long Short-Term Memory on time series problems. To increase the capability of modeling distant temporal interactions, we propose a hierarchical architecture (HLSTM) using several LSTM models and a linear layer. This novel framework is then applied to electric power consumption, real-life crime and financial data. We demonstrate in our simulations that this structure significantly improves the modeling of deep temporal connections compared to the classical architecture of LSTM and various studies in the literature. Furthermore, we analyze the sensitivity of the new architecture with respect to the hidden size of LSTM.en_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image and Video Processingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTime series predictionen_US
dc.subjectRecurrent neural networksen_US
dc.subjectRNNen_US
dc.subjectLSTMen_US
dc.subjectLstmen_US
dc.subjectCnnen_US
dc.titleTime series prediction with hierarchical recurrent modelen_US
dc.typeArticleen_US
dc.departmentTOBB ETÜen_US
dc.identifier.wosWOS:000899810400001en_US
dc.identifier.scopus2-s2.0-85143974583en_US
dc.institutionauthor-
dc.identifier.doi10.1007/s11760-022-02426-6-
dc.authorscopusid57419487300-
dc.authorscopusid58010384200-
dc.authorscopusid57219492922-
dc.authorscopusid36348487700-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

WEB OF SCIENCETM
Citations

1
checked on Apr 13, 2024

Page view(s)

14
checked on Apr 15, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.