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EA-LSTM: Evolutionary attention-based LSTM for time series prediction
Li, Youru1,2; Zhu, Zhenfeng1,2; Kong, Deqiang3; Han, Hua4,5; Zhao, Yao1,2
发表期刊KNOWLEDGE-BASED SYSTEMS
ISSN0950-7051
2019-10-01
卷号181页码:8
通讯作者Zhu, Zhenfeng(zhfzhu@bjtu.edu.cn)
摘要Time series prediction with deep learning methods, especially Long Short-term Memory Neural Network (LSTM), have scored significant achievements in recent years. Despite the fact that LSTM can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within multiple time-steps is insufficient. To address this issue, an evolutionary attention-based LSTM training with competitive random search is proposed for multivariate time series prediction. By transferring shared parameters, an evolutionary attention learning approach is introduced to LSTM. Thus, like that for biological evolution, the pattern for importance-based attention sampling can be confirmed during temporal relationship mining. To refrain from being trapped into partial optimization like traditional gradient-based methods, an evolutionary computation inspired competitive random search method is proposed, which can well configure the parameters in the attention layer. Experimental results have illustrated that the proposed model can achieve competetive prediction performance compared with other baseline methods. (C) 2019 Elsevier B.V. All rights reserved.
关键词Evolutionary computation Deep neural network Time series prediction
DOI10.1016/j.knosys.2019.05.028
收录类别SCI
语种英语
资助项目National Key Research and Development of China[2016YFB0800404] ; National Natural Science Foundation of China[61572068] ; National Natural Science Foundation of China[61532005] ; Special Program of Beijing Municipal Science & Technology Commission[Z181100000118002] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32030200] ; Fundamental Research Funds for the Central Universities of China[2018YJS032] ; National Key Research and Development of China[2016YFB0800404] ; National Natural Science Foundation of China[61572068] ; National Natural Science Foundation of China[61532005] ; Special Program of Beijing Municipal Science & Technology Commission[Z181100000118002] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32030200] ; Fundamental Research Funds for the Central Universities of China[2018YJS032]
项目资助者National Key Research and Development of China ; National Natural Science Foundation of China ; Special Program of Beijing Municipal Science & Technology Commission ; Strategic Priority Research Program of Chinese Academy of Science ; Fundamental Research Funds for the Central Universities of China
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000484873600005
出版者ELSEVIER
引用统计
被引频次:232[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/27224
专题类脑智能研究中心_微观重建与智能分析
通讯作者Zhu, Zhenfeng
作者单位1.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
2.Beijing Key Lab Adv Informat Sci & Network Techno, Beijing 100044, Peoples R China
3.Microsoft Multimedia, Beijing 100080, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
5.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
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GB/T 7714
Li, Youru,Zhu, Zhenfeng,Kong, Deqiang,et al. EA-LSTM: Evolutionary attention-based LSTM for time series prediction[J]. KNOWLEDGE-BASED SYSTEMS,2019,181:8.
APA Li, Youru,Zhu, Zhenfeng,Kong, Deqiang,Han, Hua,&Zhao, Yao.(2019).EA-LSTM: Evolutionary attention-based LSTM for time series prediction.KNOWLEDGE-BASED SYSTEMS,181,8.
MLA Li, Youru,et al."EA-LSTM: Evolutionary attention-based LSTM for time series prediction".KNOWLEDGE-BASED SYSTEMS 181(2019):8.
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