  Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series
classification have produced state-of-the-art classification results on
univariate time series. We show that replacing the LSTM with a gated recurrent
unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN)
can offer even better performance on many time series datasets. The proposed
GRU-FCN model outperforms state-of-the-art classification performance in many
univariate and multivariate time series datasets. In addition, since the GRU
uses a simpler architecture than the LSTM, it has fewer training parameters,
less training time, and a simpler hardware implementation, compared to the
LSTM-based models.
