Abstract

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.

Keywords

• Long short-term memory
• Convolutional neural network
• Attention Mechanism
• Financial data analysis
• Deep learning

Bibtex

@ARTICLE{JZhou:IEEEAccess19, 
	author={Junhao Zhou and Zhanhong He and Ya Nan Song and Hao Wang and Xiaoping Yang and Wenjuan Lian and Hong-Ning Dai}, 
	journal={IEEE Access}, 
	title={Precious Metal Price Prediction based on Deep Regularization Self-Attention Regression}, 
	year={2020}, 
	volume={8}, 
	number={}, 
	pages={2178 - 2187}, 
	doi={10.1109/ACCESS.2019.2962202}, 
}

Part of this paper was presented in in the 5th International Conference on Cloud and Big Data Computing (CBDCom), Fukuoka, Japan, 2019.