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

Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification