  Time series prediction with deep learning methods, especially long short-term
memory neural networks (LSTMs), have scored significant achievements in recent
years. Despite the fact that the LSTMs 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 the
LSTMs model. 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.
