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3DACN: 3D Augmented convolutional network for time series data

文献类型：期刊论文

作者Pei, Songwen1,2,3; Shen, Tianma1; Wang, Xianrong1; Gu, Chunhua1; Ning, Zhong2; Ye, Xiaochun3; Xiong, Naixue4
刊名INFORMATION SCIENCES
出版日期2020-03-01
卷号513页码:17-29
关键词Time series data Gated recurrent units Convolutional neural network Expectation-maximization algorithm Augmented algorithm
ISSN号0020-0255
DOI10.1016/j.ins.2019.11.040
英文摘要Time series data and non-time series data are increasing in the credit system of financial market, so that an effective and intelligent data mining model plays a critical role to analyze hybrid time series data. In addition, traditional mining models sometimes fail to converge because of imbalanced data problem. Therefore, we propose a 3D Augmented Convolutional Network (3DACN) to extract time series information and solve the serious imbalanced data problem. By using the augmented algorithm on time series data, hybrid time series data are enlarged to generate more examples on the minority classes. 3DACN ensures the latent variables with an Expectation-Maximization(EM) algorithm to improve F1 score (F1) and Area Under Curve (AUC). Experimental results show that in the benchmark of Bank database, it can gain F1 score by 81.1% and the AUC by 88.2% respectively; while in the benchmark of Credit Risk database, the 3DACN can reach high performance on F1 score by 88.1% and the AUC by 88.4%. (C) 2019 Elsevier Inc. All rights reserved.
资助项目National Science Foundation of China[61975124] ; National Science Foundation of China[61775139] ; National Science Foundation of China[61332009] ; China Postdoctoral Science Foundation[2017M610230] ; Opening Project Foundation of the State Key Lab of Computer Architecture[CARCH 201807]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000512221800002
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/14667]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Pei, Songwen
作者单位1.Univ Shanghai Sci & Technol, Sch Opt Elect & Comp Engn, Shanghai 200093, Peoples R China
2.Fudan Univ, Sch Management, Shanghai 200433, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
4.Northeastern State Univ, Dept Comp Sci, Tahlequah, OK 74464 USA
推荐引用方式
GB/T 7714		 Pei, Songwen,Shen, Tianma,Wang, Xianrong,et al. 3DACN: 3D Augmented convolutional network for time series data[J]. INFORMATION SCIENCES,2020,513:17-29.
APA Pei, Songwen.,Shen, Tianma.,Wang, Xianrong.,Gu, Chunhua.,Ning, Zhong.,...&Xiong, Naixue.(2020).3DACN: 3D Augmented convolutional network for time series data.INFORMATION SCIENCES,513,17-29.
MLA Pei, Songwen,et al."3DACN: 3D Augmented convolutional network for time series data".INFORMATION SCIENCES 513(2020):17-29.

入库方式： OAI收割

来源：计算技术研究所

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