… long-term traffic flow forecasting method, which we call the Attention Calibration Encoder-Decoder
(… seq2seq model is applied to traffic flow forecasting. The LSTM is used to capture the …

… With the rapid development of data centers, the large-scale network traffic prediction
requires more suitable methods to deal with the complex properties of high-dimension, longrange …

… It consists of two parts: the encoder for modeling traffic dynamics and decoder for … predicted
spatio-temporal matrix by the decoder that is another ConvLSTM. As the final hidden state …

… results (5min/10min/15min), while Table III focuses on longer term predictions (30min/45min/60min).
The results indicate that the SVR model was not able to model the temporal …

… an encoder-decoder model with temporal attention mechanism for multi-step forward traffic
flow prediction task, which uses LSTM as the encoder … forecasting performance and long time-…

… forecasting. We enhance the encoder-decoder architecture by adopting gated dilated …
by weekday respectively are used to capture the weekly patterns from a short to long term. …

… In summary, the CNN-based models are suitable for current human traffic prediction. Recently,
many scholars have presented several approaches based on CNNs [5], [29], [30], [31], [32]…

… longterm traffic prediction. STANN captures the spatial-temporal dependencies based on
the encoder-decoder … we enhance the encoder-decoder architecture for traffic prediction, by …

… different prediction time steps in the long time horizon, we add a transform attention layer
between the encoder and the decoder. It models the direct relationship between each future …

… end framework for traffic prediction, which can model spatial, short-term and longterm periodical
… After encoder-decoder, we use a feedforward neural network to obtain the final output of …