Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification
Machine Learning
2019-10-02 v3 Machine Learning
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.
Cite
@article{arxiv.1812.07683,
title = {Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification},
author = {Nelly Elsayed and Anthony S. Maida and Magdy Bayoumi},
journal= {arXiv preprint arXiv:1812.07683},
year = {2019}
}
Comments
The paper modified and has several new results