Hybrid Long Short-Term Memory prediction model improved by particle swarm optimization with sine and cosine factors.
The Long Short-Term Memory network of deep learning neural network is widely used to predict stock price in financial field. In order to optimize the accuracy of stock price prediction by LSTM network, this paper firstly uses principal component analysis method to extract various influencing indexes...
| Publicado en: | SHS Web of Conferences Vol. 170; pp. 1 - 5 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
EDP Sciences
6/14/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=164639386&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 164639386 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24165182 FT5R jtl: SHS Web of Conferences issn: 24165182 maglogo: N pubinfo: dt: 6/14/2023 vid: 170 pid: 76090 pub: EDP Sciences artinfo: ui: 164639386 10.1051/shsconf/202317003019 ppf: 1 ppct: 4 formats: tig: atl: Hybrid Long Short-Term Memory prediction model improved by particle swarm optimization with sine and cosine factors. aug: au: Pei, Wanyue Yang, Yanfei affil: International college, Zhengzhou University, Zhengzhou, Henan, China College of Textile, Zhongyuan University of Technology, Zhengzhou, Henan, China sug: ab: The Long Short-Term Memory network of deep learning neural network is widely used to predict stock price in financial field. In order to optimize the accuracy of stock price prediction by LSTM network, this paper firstly uses principal component analysis method to extract various influencing indexes of stock. Then, use Circle mapping method to select the initial value more evenly, use sine and cosine factors to improve factors of particle swarm optimization algorithm, so as to find the optimal parameters of LSTM model more effectively. Finally, the optimization results of IPSO algorithm are substituted into the LSTM model for the regression prediction with the principal components. Through empirical analysis and comparative test, the results show that the improved particle swarm optimization algorithm proposed in this paper has better optimization effect, is not prone to local optimal problems, and the prediction model based on this method has higher prediction accuracy. pubtype: Conference Proceedings doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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