A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.

中文翻译：

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eep时间卷积网络用于时间序列分类

与复杂数据功能匹配的神经网络很可能会提高分类性能，因为它能够学习高度变化的数据的有用方面。在这项工作中，时间序列数据的时间上下文被选择为通过网络进行学习的数据的有用方面。通过利用网络每个级别上时间序列数据的组成局部性，可以在不同的时间尺度上逐层提取不变不变特征。通过基于串联操作的一组数据处理操作，可以使时间上下文对网络的较深层可用。本文介绍了一种针对修订后的网络的匹配学习算法。它在反向传播路径中使用梯度路由。由于权重可以适当地预先训练，因此本文中提出的框架可以在不使网络过度适应数据的情况下获得更好的概括性。可以将原始时间的多元时间序列数据端到端使用，而无需进行手工特征处理或数据转换。脑电图信号和人类活动信号的数据实验表明，在建议的网络的较深层中，通过适当数量的串联，可以提高信号分类的性能。