Time Series Classification (TSC) is an important and challenging
problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a
few have considered Deep Neural Networks (DNNs) to perform this task. This
is surprising as deep learning has seen very successful applications in the last
years. DNNs have indeed revolutionized the field of computer vision especially
with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and
audio can also be processed with DNNs to reach state of the art performance
for document classification and speech recognition. In this article, we study
the current state of the art performance of deep learning algorithms for TSC
by presenting an empirical study of the most recent DNN architectures for
TSC. We give an overview of the most successful deep learning applications
in various time series domains under a unified taxonomy of DNNs for TSC.
We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated
them on a univariate TSC benchmark (the UCR archive) and 12 multivariate
time series datasets. By training 8,730 deep learning models on 97 time series
datasets, we propose the most exhaustive study of DNNs for TSC to date.
