Computer Science ›› 2020, Vol. 47 ›› Issue (12): 125-130.doi: 10.11896/jsjkx.200700050

Special Issue: Big Data & Data Scinece

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Financial Data Prediction Method Based on Deep LSTM and Attention Mechanism

LIU Chong, DU Jun-ping   

  1. Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing 100876,China
  • Received:2020-07-08 Revised:2020-09-03 Online:2020-12-15 Published:2020-12-17
  • About author:LIU Chong,born in 1995postgraduateis a member of China Computer Federation.His main research interests include nature language processingcomputer vision and deep learning.
    DU Jun-ping,born in 1963Ph.Dprofessoris a fellow of China ComputerFederation and CAAI.Her main research interests include artificial intelligenceimage processing and pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(61902037,61532006,61772083,61802028) and Science and Technology Major Project of Guangxi (Guike AA18118054).

Abstract: With the rapid development of the Internetfinancial markets generate a large amount of online financial data every daysuch as the number of daily transactions and the total amount of transactions.The dynamic prediction of financial market data has become a research hotspot in recent years.Howeverthe financial market has a large amount of datamany input sequencesand changes over time.Aiming at solving these problemsthis paper proposes a financial data prediction model based on deep LSTM and attention mechanism.Firstthe model can handle complex financial market data which are mainly multi-sequence data.Secondthe model uses deep LSTM networks to model financial datasolves the problem of long dependence between dataand can learn more complex market dynamic characteristics.Finallythe model introduces the attention mechanismwhich makes the data of different time have different importance to the prediction and make the prediction more accurate.Experiments on real large data sets show that the proposed model has the characteristics of high accuracy and good stability in the field of dynamic prediction.

Key words: Attention mechanism, Deep LSTM, Financial forecasting, Sequence model

CLC Number: 

  • TP391
[1] ZHANG J,SUN Q.Research on Financing Cost of Small and Medium-Sized Enterprises by Internet Finance[J].Open Journal of Social Sciences,2017,5(11):95.
[2] LIN Y H,CHEN C F.Research on Enterprise Financial RiskEvaluation Based on Association Rules[J].Friends of Accounting,2017(1):32-35.
[3] LIU J X,JIA X Y.A Multi-label Classification Algorithm Based on Association Rules Mining[J].Journal of Software,2017,28(11):2865-2878.
[4] GREFF K,SRIVASTAVA R K,KOUTNÍK J,et al.LSTM:A search space odyssey[J].IEEE Transactions on Neural Networks and Learning Systems,2016,28(10):2222-2232.
[5] MERITY S,KESKAR N S,SOCHER R.Regularizing and optimizing LSTM language models[J].arXiv:1708.02182.
[6] ZHAO Z,CHEN W,WU X,et al.LSTM network:a deep learning approach for short-term traffic forecast[J].IET Intelligent Transport Systems,2017,11(2):68-75.
[7] KARIM F,MAJUMDAR S,DARABI H,et al.LSTM fully convolutional networks for time series classification[J].IEEE Access,2017,6:1662-1669.
[8] FU R,ZHANG Z,LI L.Using LSTM and GRU neural network methods for traffic flow prediction[C]//2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).IEEE,2016:324-328.
[9] CHOI H,CHO K,BENGIO Y.Fine-grained attention mechanism for neural machine translation[J].Neurocomputing,2018,284:171-176.
[10] TILK O,ALUMÄE T.Bidirectional Recurrent Neural Network with Attention Mechanism for Punctuation Restoration[C]//Interspeech.2016:3047-3051.
[11] WANG J,SUN T,LIU B,et al.CLVSA:A convolutional LSTM based variational sequence-to-sequence model with attention for predicting trends of financial markets[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence.AAAI Press,2019:3705-3711.
[12] CHEN L,CHI Y,GUAN Y,et al.A Hybrid Attention-BasedEMD-LSTM Model for Financial Time Series Prediction[C]//2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD).IEEE,2019:113-118.
[13] JIANG M,WANG J,LAN M,et al.An effective gated and attention-based neural network model for fine-grained financial target-dependent sentiment analysis[C]//International Conference on Knowledge Science,Engineering and Management.Springer,Cham,2017:42-54.
[14] CONTRERAS J,ESPINOLA R,NOGALES F J,et al.ARIMA models to predict next-day electricity prices[J].IEEE Transactions on Power Systems,2003,18(3):1014-1020.
[15] GAO Y,GLOWACKA D.Deep gate recurrent neural network[C]//Asian Conference on Machine Learning.2016:350-365.
[16] HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780.
[17] ZHAO H K,WU L K,LI H,et al.Predicting the Dynamics in Internet Finance Based on Deep Neural Network Structure[J].Journal of Computer Research and Development,2019,56(8):1621-1631.
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