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arXiv · 1812.07683

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

Nelly Elsayed·Anthony S. Maida·Magdy Bayoumi
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Abstract

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.

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Nelly Elsayed, Anthony S. Maida, Magdy Bayoumi. 2019-02-20. Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification. https://doi.org/10.14569/ijacsa.2019.0100582

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