基于联合特征学习的多尺度卷积神经网络在外汇交易市场中的应用  

A Multiscale Convolutional Neural Network for Forex Trading Using Joint Feature Learning

作　　者：陈希远 朱佳 CHEN Xi-yuan;ZHU Jia(School of Computer,South China Normal University,Guangzhou 510631,China)

机构地区：[1]华南师范大学计算机学院,广东广州510631

出　　处：《计算机与现代化》2018年第9期122-126,共5页Computer and Modernization

摘　　要：卷积神经网络(CNN)已经引起了计算机视觉领域的变革。本文探讨CNN的一个具体应用:已知价格在过去一段时间内的波动图后,利用CNN对外汇市场的价格进行预测,然后将预测结果用于外汇交易,最终获利。采用联合特征学习机制,创建一种新的可处理多种特征的多尺度CNN应用框架。实验结果表明,相比于只考虑图像特征的传统CNN及其他机器学习算法,本文算法的实用性更强。Convolutional neural networks（ CNNs） have revolutionized the field of computer vision. In this paper,we explore a particular application of CNNs： using CNNs to predict movements of forex prices from a picture of a time series of past price fluctuations,with the ultimate goal of using them for forex trading in order to make a profit. The main contribution of this research is to set up a novel architecture that uses multiscale CNNs to handle various kinds of features with a joint feature learning mechanism. Experimental results show our approach is more feasible compared with the basic CNNs using only image feature and other traditional machine learning methods.

关 键 词：多尺度卷积神经网络 联合特征学习 外汇交易 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]