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dc.contributor.authorHe, Zhanhong
dc.contributor.authorZhou, Junhao
dc.contributor.authorDai, Hong-Ning
dc.contributor.authorWang, Hao
dc.date.accessioned2019-11-29T13:52:28Z
dc.date.available2019-11-29T13:52:28Z
dc.date.created2019-11-26T21:44:26Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-3024-8
dc.identifier.urihttp://hdl.handle.net/11250/2631121
dc.description.abstractAn accurate prediction is certainly significant in financial data analysis. Investors have used a series of econometric techniques on pricing, stock selection and risk management but few of them have found great success due to the fact that most of them only are purely based on a single scheme. Recent advances in deep learning methods have also demonstrated the outstanding performance in the fields of image recognition and sentiment analysis. In this paper, we originally propose a novel gold price forecast method based on the integration of Long Short-Term Memory Neural Networks (LSTM) and Convolutional Neural Networks (CNN) with Attention Mechanism (denoted to LSTM-Attention-CNN model). Particularly, the LSTM-Attention-CNN model consists of three components: the LSTM component, Attention Mechanism and the CNN component. The LSTM component enables to harness the sequential order of daily gold price. Meanwhile, the Attention Mechanism assigns different attention weights on the new encoding method from LSTM component to enhance the extraction of the temporal and spatial features. In addition, the CNN component enables to capture the local patterns and abstract the spatial features. Extensive experiments on real dataset collected from World Gold Council show that our proposed approach outperforms other conventional financial forecast methods.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.ispartofProceedings of IEEE 5th International Conference on Cloud and Big Data Computing (CBDCom 2019)
dc.titleGold Price Forecast Based on LSTM-CNN Modelnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber1046-1053nb_NO
dc.identifier.doihttps://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00188
dc.identifier.cristin1752809
dc.description.localcode© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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