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

Zhiguang Wang, Weizhong Yan, Tim Oates

Published 2016-11-20Version 1

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 FCN achieves premium performance to other state-of-the-art approaches. Our exploration of the very deep neural networks with the ResNet structure achieves competitive performance under the same simple experiment settings. The simple MLP baseline is also comparable to the 1NN-DTW as a previous golden baseline. 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 of our models, learned features, network structures and the classification semantics.