Journal of Guangxi Normal University(Natural Science Edition) ›› 2019, Vol. 37 ›› Issue (2): 82-89.doi: 10.16088/j.issn.1001-6600.2019.02.010

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Predicting Financial Time Series Based on Gated Recurrent Unit Neural Network

ZHANG Jinlei, LUO Yuling*, FU Qiang   

  1. College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004, China
  • Received:2018-08-13 Online:2019-04-25 Published:2019-04-28

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Abstract: To solve the long-term dependence of the Recurrent Neural Networks (RNN), the Gated Recurrent Unit (GRU) neural network is proposed as a variant of the RNN. By inheriting the excellent memory ability of the RNN to time series, the GRU overcomes this long-term dependence problem. For the long-term dependence of time series in financial time series, the GRU extension is applied to financial time series prediction. A financial time series prediction model based on differential operation and GRU neural network is proposed in this paper. The model is capable of handling complex features of financial time series data such as non-linearity, non-stationary and sequence correlation. It is used to predict the adjusted close price of the Standard & Poor’s (S&P) 500 stock index in this work. The experimental results show that the differencing operation can improve the generalization ability and the prediction accuracy of the GRU neural network, and the proposed method can perform a better prediction for the financial time series than the conventional approach, with a relatively low computing overhead.

Key words: recurrent neural network, gated recurrent unit (GRU), differencing operation, financial time series prediction, deep learning

CLC Number: 

  • TP183
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