EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction

9 Nov 2018 Youru Li Zhenfeng Zhu Deqiang Kong Hua Han Yao Zhao

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... (read more)

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Methods used in the Paper


METHOD TYPE
Random Search
Hyperparameter Search
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
LSTM
Recurrent Neural Networks