標題: Financial Time-series Data Analysis using Deep Convolutional Neural Networks
作者: Chen, Jou-Fan
Chen, Wei-Lun
Huang, Chun-Ping
Huang, Szu-Hao
Chen, An-Pin
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: Deep learning;data visualization;trend prediction;convolutional neural networks;machine learning
公開日期: 1-Jan-2016
摘要: A novel financial time-series analysis method based on deep learning technique is proposed in this paper. In recent years, the explosive growth of deep learning researches have led to several successful applications in various artificial intelligence and multimedia fields, such as visual recognition, robot vision, and natural language processing. In this paper, we focus on the time-series data processing and prediction in financial markets. Traditional feature extraction approaches in intelligent trading decision support system are used to applying several technical indicators and expert rules to extract numerical features. The major contribution of this paper is to improve the algorithmic trading framework with the proposed planar feature representation methods and deep convolutional neural networks (CNN). The proposed system is implemented and benchmarked in the historical datasets of Taiwan Stock Index Futures. The experimental results show that the deep learning technique is effective in our trading simulation application, and may have greater potentialities to model the noisy financial data and complex social science problems. In the future, we expected that the proposed methods and deep learning framework could be applied to more innovative applications in the next financial technology (FinTech) generation.
URI: http://dx.doi.org/10.1109/CCBD.2016.51
http://hdl.handle.net/11536/146142
ISSN: 2378-3680
DOI: 10.1109/CCBD.2016.51
期刊: 2016 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA (CCBD)
起始頁: 87
結束頁: 92
Appears in Collections:Conferences Paper