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.
翻译:用于时间序列分类的混合式LSTM-全革命网络(LSTM-FCN)生成了单一时间序列的最新分类结果,我们表明,用一个封闭的经常性单位(GRU)取代LSTM,以创建GRU-全革命网络混合模型(GRU-FCN),在许多时间序列数据集中可以提供更好的性能。拟议的GRU-FCN模型在许多单象和多变时间序列数据集中比最新水平分类性能要好。此外,由于GRU使用比LSTM更简单的结构,因此与基于LSTM的模型相比,它的培训参数、培训时间和硬件实施更简单。