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

Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline