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

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• 作者：Martin Lä ; ngkvist ; ^{martin.langkvist@oru.se" class="auth_mail} ; Lars Karlsson ^{lars.karlsson@oru.se" class="auth_mail} ; Amy Loutfi ^{amy.loutfi@oru.se" class="auth_mail}
• 关键词：Time-series ; Unsupervised feature learning ; Deep learning
• 刊名：Pattern Recognition Letters
• 出版年：1 June, 2014
• 年：2014
• 卷：42
• 期：Complete
• 页码：11-24
• 全文大小：1170 K

文摘

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 alternatively have contributed to modifications of feature learning algorithms to take into account the challenges present in time-series data.