标题:Forecasting of Forex Time Series Data Based on Deep Learning
作者:Ni, Lina; Li, Yujie; Wang, Xiao; Zhang, Jinquan; Yu, Jiguo; Qi, Chengming
通讯作者:Zhang, JQ;Zhang, JQ
作者机构:[Ni, Lina; Li, Yujie; Wang, Xiao; Zhang, Jinquan] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao 266590, Shandong, Peoples R China.; [Ni 更多
会议名称:7th International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)
会议日期:OCT 19-21, 2018
来源:2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS
出版年:2019
卷:147
页码:647-652
DOI:10.1016/j.procs.2019.01.189
关键词:Deep learning; Recurrent neural network; Convolutional neural network;; Foreign Exchange Rate; Time series analysis
摘要:This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method. On the exchange rate data of nine major foreign exchange currencies, the experimental comparison of the forecasting method shows that the C-RNN foreign exchange time series data prediction method constructed in this paper has better applicability and higher accuracy. (C) 2019 The Authors. Published by Elsevier B.V.
收录类别:CPCI-S
资源类型:会议论文
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