期刊
KNOWLEDGE-BASED SYSTEMS
卷 181, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2019.05.028
关键词
Evolutionary computation; Deep neural network; Time series prediction
资金
- National Key Research and Development of China [2016YFB0800404]
- National Natural Science Foundation of China [61572068, 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]
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.
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