Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 5
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 empirically 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 without further changes to the model. Our empirical study showed that the proposed GRU-FCN model also outperforms the state-of-the-art classification performance in many univariate time series datasets without additional supporting algorithms requirement. Furthermore, since the GRU uses simpler architecture than the LSTM, it has fewer training parameters, less training time, smaller memory storage requirements, and simpler hardware implementation, compared to the LSTM-based models.
Authors and Affiliations
Nelly Elsayed, Anthony S Maida, Magdy Bayoumi
Keywords
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- EP ID EP579357
- DOI 10.14569/IJACSA.2019.0100582
- Views 77
- Downloads 0
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