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 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.

Keywords

• Long short-term memory
• Convolutional neural network
• Gold price prediction
• Deep learning

Bibtex

@INPROCEEDINGS{ZHe:CBDCOM19, 
	author={Zhanhong He and Junhao Zhou and Hong-Ning Dai and Hao Wang}, 
	booktitle={The 5th International Conference on Cloud and Big Data Computing (CBDCom)}, 
	title={Gold Price Forecast based on LSTM-CNN Model}, 
	year={2019}, 
	volume={}, 
	number={}, 
	pages={1-6}, 
}