To make sequence-to-sequence predictions using a LSTM, we use an encoder-decoder architecture. The LSTM encoder-decoder consists of two LSTMs. The first LSTM, or the encoder, processes an input ...

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Some of the applications are: Before going deeper into the network, we should have some prior knowledge about the RNN and LSTM models. This can be obtained by using this article. Now, let’s have a ...

In this paper, we propose an encoder-decoder model with temporal attention mechanism for multi-step forward traffic flow prediction task, which uses LSTM as the encoder and decoder to learn the long ...

To this end, we used daily precipitation data over the period 1988-2010 as input of the models, such as the Long Short-Term Memory (LSTM) and Recurrent Gate Networks (GRU) to simulate river discharge ...