Precious Metal Price Prediction Based on Deep Regularization Self-Attention Regression

Junhao ZHOU, Zhanhong HE, Ya Nan SONG*, Hao WANG, Xiaoping YANG, Wenjuan LIAN, Hong Ning DAI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

2 Citations (Scopus)


It is non-trivial to predict the prices of precious metals since a number of factors can affect the fluctuations of precious metal prices. Either parametric models or machine learning models cannot accurately forecast the precious metal prices. Though deep learning approaches show their strengths in extracting key features from complicated data, they have the limitations of learning localization and losing some temporal and spatial features. The recent advances in attention mechanisms bring the opportunities to overcome the limitation of deep learning models. In this paper, we originally propose a Regularization Self-Attention Regression Model for precious metal price prediction. In particular, the proposed RSAR model consists of convolutional neural network (CNN) component and Long Short-Term Memory Neural Networks (LSTM) component. Integrating with self-attention mechanism, this model can extract both spatial and temporal features from precious metal price data. Meanwhile, the proper configuration of regularization functions can also lead to the further performance improvement. Extensive experiments on realistic precious metal price dataset show that our proposed approach outperforms other conventional machine learning and deep learning methods.

Original languageEnglish
Article number8943215
Pages (from-to)2178-2187
Number of pages10
JournalIEEE Access
Early online date25 Dec 2019
Publication statusPublished - 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NFSC) under Grant 61672170, in part by the NSFC-Guangdong Joint Fund under Grant U1401251, in part by the Science and Technology Planning Project of Guangdong Province under Grant 2017A050501035, and in part by the Science and Technology Program of Guangzhou under Grant 201807010058.

Publisher Copyright:
© 2013 IEEE.


  • attention mechanism
  • convolutional neural network
  • deep learning
  • financial data analysis
  • Long short-term memory


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