  Time series classification problems exist in many fields and have been
explored for a couple of decades. However, they still remain challenging, and
their solutions need to be further improved for real-world applications in
terms of both accuracy and efficiency. In this paper, we propose a hybrid
neural architecture, called Self-Attentive Recurrent Convolutional Networks
(SARCoN), to learn multi-faceted representations for univariate time series.
SARCoN is the synthesis of long short-term memory networks with self-attentive
mechanisms and Fully Convolutional Networks, which work in parallel to learn
the representations of univariate time series from different perspectives. The
component modules of the proposed architecture are trained jointly in an
end-to-end manner and they classify the input time series in a cooperative way.
Due to its domain-agnostic nature, SARCoN is able to generalize a diversity of
domain tasks. Our experimental results show that, compared to the
state-of-the-art approaches for time series classification, the proposed
architecture can achieve remarkable improvements for a set of univariate time
series benchmarks from the UCR repository. Moreover, the self-attention and the
global average pooling in the proposed architecture enable visible
interpretability by facilitating the identification of the contribution regions
of the original time series. An overall analysis confirms that multi-faceted
representations of time series aid in capturing deep temporal corrections
within complex time series, which is essential for the improvement of time
series classification performance. Our work provides a novel angle that deepens
the understanding of time series classification, qualifying our proposed model
as an ideal choice for real-world applications.
