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Applied Mathematics and Nonlinear Sciences
Volume 6 (2021): Numero 1 (January 2021)
Accesso libero
Predicting stock high price using forecast error with recurrent neural network
Zhiguo Bao
Zhiguo Bao
,
Qing Wei
Qing Wei
,
Tingyu Zhou
Tingyu Zhou
,
Xin Jiang
Xin Jiang
e
Takahiro Watanabe
Takahiro Watanabe
| 25 mag 2021
Applied Mathematics and Nonlinear Sciences
Volume 6 (2021): Numero 1 (January 2021)
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Pubblicato online:
25 mag 2021
Pagine:
283 - 292
Ricevuto:
24 dic 2020
Accettato:
11 apr 2021
DOI:
https://doi.org/10.2478/amns.2021.2.00009
Parole chiave
stock price prediction
,
recurrent neural network
,
long short-term memory network
,
gated recurrent unit
© 2021 Zhiguo Bao et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Zhiguo Bao
School of Computer and Information Engineering, Henan University of Economics and Law
Zhengzhou Henan, China
Qing Wei
School of Computer and Information Engineering, Henan University of Economics and Law
Zhengzhou Henan, China
School of Management Engineering, Capital University of Economics and Business
Fengtai Beijing, China
Tingyu Zhou
Graduate School of Information, Production and Systems, Waseda University
Kitakyushu, Japan
Xin Jiang
National Institute of Technology, Kitakyushu College
Kitakyushu, Japan
Takahiro Watanabe
Graduate School of Information, Production and Systems, Waseda University
Kitakyushu, Japan