Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/167496
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dc.contributor.authorKuan, Soon Yeeen_US
dc.date.accessioned2023-05-29T08:12:35Z-
dc.date.available2023-05-29T08:12:35Z-
dc.date.issued2023-
dc.identifier.citationKuan, S. Y. (2023). Deep GRU neural networks for gold price prediction. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167496en_US
dc.identifier.urihttps://hdl.handle.net/10356/167496-
dc.description.abstractGold is a versatile material in high demand by both corporates and investors. Hence, the ability to predict gold prices would be of great assistance in acquiring gold. Gated Recurrent Unit (GRU) is a recurrent neural network that can be used to predict gold prices. The aim of this paper was to improve upon the best known GRU model for predicting gold prices.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA3283-221en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleDeep GRU neural networks for gold price predictionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorWang Lipoen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailELPWang@ntu.edu.sgen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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