A review of unsupervised feature learning and deep learning for time-series modeling

Martin Längkvist, Lars Karlsson, Amy Loutfi
<span title="">2014</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6r4znskbk5h2ngu345slqsm6eu" style="color: black;">Pattern Recognition Letters</a> </i> &nbsp;
This paper gives a review of the recent developments in deep learning and unsupervised feature learning for time-series problems. While these techniques have shown promise for modeling static data, such as computer vision, applying them to time-series data is gaining increasing attention. This paper overviews the particular challenges present in time-series data and provides a review of the works that have either applied time-series data to unsupervised feature learning algorithms or
more &raquo; ... ly have contributed to modications of feature learning algorithms to take into account the challenges present in time-series data.
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