Echo State Networks (ESNs) are efficient recurrent neural networks (RNNs) which have been successfully applied to time series modeling tasks. However, ESNs are unable to capture the history information far from the current time step, since the echo state at the present step of ESNs mostly impacted by the previous one. Thus, ESN may have difficulty in capturing the long-term dependencies of temporal data. In this paper, we propose an end-to-end model named Echo Memory-Augmented Network (EMAN) for time series classification. An EMAN consists of an echo memory-augmented encoder and a multi-scale convolutional learner. First, the time series is fed into the reservoir of an ESN to produce the echo states, which are all collected into an echo memory matrix along with the time steps. After that, we design an echo memory-augmented mechanism employing the sparse learnable attention to the echo memory matrix to obtain the Echo Memory-Augmented Representations (EMARs). In this way, the input time series is encoded into the EMARs with enhancing the temporal memory of the ESN. We then use multi-scale convolutions with the max-over-time pooling to extract the most discriminative features from the EMARs. Finally, a fully-connected layer and a softmax layer calculate the probability distribution on categories. Experiments conducted on extensive time series datasets show that EMAN is state-of-the-art compared to existing time series classification methods. The visualization analysis also demonstrates the effectiveness of enhancing the temporal memory of the ESN.

回声状态网络（ESN）是高效的递归神经网络（RNN），已成功应用于时间序列建模任务。但是，ESN无法捕获距当前时间段很远的历史信息，因为ESN当前步骤中的回声状态主要受到前一个的影响。因此，ESN可能难以捕获时间数据的长期依赖性。在本文中，我们提出了一个名为Echo内存增强网络（EMAN）的端到端模型，用于时间序列分类。EMAN由回波记忆增强编码器和多尺度卷积学习器组成。首先，将时间序列输入到ESN的存储库中以产生回波状态，并将这些回波状态与时间步长一起收集到回波存储矩阵中。之后，我们设计了一种对回声记忆矩阵的稀疏可学注意力，从而获得了回声记忆增强表示（EMAR）。以这种方式，通过增强ESN的时间记忆，将输入时间序列编码到EMAR中。然后，我们将多尺度卷积与max-time-time池一起使用，以从EMAR中提取最具区别性的特征。最后，完全连接的层和s​​oftmax层计算类别的概率分布。在大量时间序列数据集上进行的实验表明，与现有的时间序列分类方法相比，EMAN是最先进的。可视化分析还证明了增强ESN的时间记忆的有效性。