Source: https://ieeexplore.ieee.org/abstract/document/9090138
Quantitative and Qualitative Analysis of Time-Series Classification Using Deep Learning
Publisher:
IEEE
Under a Creative Commons License
Abstract:
Time-series classification is
utilized in a variety of applications leading to the development of many
data mining techniques for time-series analysis. Among the broad range
of time-series classification algorithms, recent studies are considering
the impact of deep learning methods on time-series classification
tasks. The quantity of related publications requires a bibliometric
study to explore most prominent keywords, countries, sources and
research clusters. The paper conducts a bibliometric analysis on related
publications in time-series classification, adopted from Scopus
database between 2010 and 2019. Through keywords co-occurrence analysis,
a visual network structure of top keywords in time-series
classification research has been produced and deep learning has been
introduced as the most common topic by additional inquiry of the
bibliography. The paper continues by exploring the publication trends of
recent deep learning approaches for time-series classification. The
annual number of publications, the productive and collaborative
countries, the growth rate of sources, the most occurred keywords and
the research collaborations are revealed from the bibliometric analysis
within the study period. The research field has been broken down into
three main categories as different frameworks of deep neural networks,
different applications in remote sensing and also in signal processing
for time-series classification tasks. The qualitative analysis
highlights the categories of top citation rate papers by describing them
in details.
Published in:
IEEE Access
(
Volume: 8
)
The graphical workflow of
bibliometric and qualitative analysis on the usage of deep learning
algorithms in time-series classification-related literature published
within...
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