EA-LSTM: Evolutionary attention-based LSTM for time series prediction
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 feat...
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| Published in | Knowledge-based systems Vol. 181; p. 104785 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
01.10.2019
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 104785 |
| Author | Han, Hua Zhao, Yao Li, Youru Zhu, Zhenfeng Kong, Deqiang |
| Author_xml | – sequence: 1 givenname: Youru surname: Li fullname: Li, Youru organization: Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China – sequence: 2 givenname: Zhenfeng surname: Zhu fullname: Zhu, Zhenfeng email: zhfzhu@bjtu.edu.cn organization: Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China – sequence: 3 givenname: Deqiang surname: Kong fullname: Kong, Deqiang organization: Microsoft Multimedia, Beijing, 100080, China – sequence: 4 givenname: Hua surname: Han fullname: Han, Hua organization: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China – sequence: 5 givenname: Yao surname: Zhao fullname: Zhao, Yao organization: Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China |
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