  Deep neural networks have revolutionized many fields such as computer vision
and natural language processing. Inspired by this recent success, deep learning
started to show promising results for Time Series Classification (TSC).
However, neural networks are still behind the state-of-the-art TSC algorithms,
that are currently composed of ensembles of 37 non deep learning based
classifiers. We attribute this gap in performance due to the lack of neural
network ensembles for TSC. Therefore in this paper, we show how an ensemble of
60 deep learning models can significantly improve upon the current
state-of-the-art performance of neural networks for TSC, when evaluated over
the UCR/UEA archive: the largest publicly available benchmark for time series
analysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is
the first time series classifier to outperform COTE while reaching similar
performance to the current state-of-the-art ensemble HIVE-COTE.
