Gold price forecast based on LSTM-CNN model

Zhanhong HE, Junhao ZHOU, Hong Ning DAI, Hao WANG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

7 Citations (Scopus)

Abstract

An accurate prediction is certainly significant in financial data analysis. Investors have used a series of econometric techniques on pricing, stock selection and risk management but few of them have found great success due to the fact that most of them only are purely based on a single scheme. Recent advances in deep learning methods have also demonstrated the outstanding performance in the fields of image recognition and sentiment analysis. In this paper, we originally propose a novel gold price forecast method based on the integration of Long Short-Term Memory Neural Networks (LSTM) and Convolutional Neural Networks (CNN) with Attention Mechanism (denoted to LSTM-Attention-CNN model). Particularly, the LSTM-Attention-CNN model consists of three components: the LSTM component, Attention Mechanism and the CNN component. The LSTM component enables to harness the sequential order of daily gold price. Meanwhile, the Attention Mechanism assigns different attention weights on the new encoding method from LSTM component to enhance the extraction of the temporal and spatial features. In addition, the CNN component enables to capture the local patterns and abstract the spatial features. Extensive experiments on real dataset collected from World Gold Council show that our proposed approach outperforms other conventional financial forecast methods.

Original languageEnglish
Title of host publicationProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1046-1053
Number of pages8
ISBN (Electronic)9781728130248
DOIs
Publication statusPublished - Aug 2019
Externally publishedYes
Event17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 - Fukuoka, Japan
Duration: 5 Aug 20198 Aug 2019

Publication series

NameProceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

Conference

Conference17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
Country/TerritoryJapan
CityFukuoka
Period5/08/198/08/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Attention Mechanism
  • Convolutional neural network
  • Deep learning
  • Financial data analysis
  • Gold price prediction
  • Long short term memory
  • Machine learning

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