An interpretable LSTM neural network for autoregressive exogenous model

14 Apr 2018 Tian Guo Tao Lin Yao Lu

In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance... (read more)

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Methods used in the Paper


METHOD TYPE
LINE
Graph Embeddings
Sigmoid Activation
Activation Functions
Tanh Activation
Activation Functions
LSTM
Recurrent Neural Networks