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Volume 39 Issue 1
Jan.  2021
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Article Navigation >  DIGITAL TECHNOLOGY & APPLICATION  > 2021  >  39(1): 157-159
LIU Chao-yun, XIE Wei. Buiding Mulit-level Recurrent Neural Network Structure to Analyse FinancialIndice Data[J]. DIGITAL TECHNOLOGY & APPLICATION, 2021, 39(1): 157-159. doi: 10.19695/j.cnki.cn12-1369.2021.01.49
Citation: LIU Chao-yun, XIE Wei. Buiding Mulit-level Recurrent Neural Network Structure to Analyse Financial Indice Data[J]. DIGITAL TECHNOLOGY & APPLICATION, 2021, 39(1): 157-159. doi: 10.19695/j.cnki.cn12-1369.2021.01.49
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Buiding Mulit-level Recurrent Neural Network Structure to Analyse Financial Indice Data

doi: 10.19695/j.cnki.cn12-1369.2021.01.49

  • 1.China United Network Telecommunications Corporation, Nanning Guangxi 530022; 2.Guangxi University of Finance and Economics, Nanning Guangxi 530022

  • Received Date: 2020-12-07
  • Rev Recd Date: 2021-01-17
    • Available Online: 2021-09-23
  • Publish Date: 2021-01-25
    Abstract
  • We propose a kind of recurrent neural network structure for analysis stock indicators and its composed indicator sets which are used for predicting stock trend. Traditional method to analysis stock trend is using kinds of indicators algorithm, but estimation of effective indicators need long period testing time to evaluate. We compose a multiple rnn cell layers neural network to receive stock data, and output prediction result data basing on sets of indicators’ data which are put through into the network as well. With comparing prediction result and real data, it is helpful to choose composition of stock indicators for investment decision strategy and saving time.

     

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    冯先成,周密,徐川.基于 Android 的百度地图多功能实现[J].武汉工程大学学报,2016(5):490-494.
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