We propose a simple but strong baseline for time series classification from
scratch with deep neural networks. Our proposed baseline models are pure
end-to-end without any heavy preprocessing on the raw data or feature crafting.
The proposed Fully Convolutional Network (FCN) achieves premium performance to
other state-of-the-art approaches and our exploration of the very deep neural
networks with the ResNet structure is also competitive. The global average
pooling in our convolutional model enables the exploitation of the Class
Activation Map (CAM) to find out the contributing region in the raw data for
the specific labels. Our models provides a simple choice for the real world
application and a good starting point for the future research. An overall
analysis is provided to discuss the generalization capability of our models,
learned features, network structures and the classification semantics.

提出了用深度神经网络从头开始进行时间序列分类的简单但强大的基准线。通过提出的全卷积网络（FCN）和 ResNet 结构的非常深层神经网络的探索，实现了优越的性能。我们卷积模型中的全局平均池化即可利用类激活映射（CAM）来查找特定标签的原始数据中的贡献区域，为现实世界的应用提供了简单的选择和未来研究的良好起点，同时提供了总体分析来讨论我们的模型，学习特征，网络结构和分类语义的泛化能力。