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

• Published in: Pattern recognition letters Vol. 42; no. 1; pp. 11 - 24
• Main Authors: Längkvist, Martin, Karlsson, Lars, Loutfi, Amy
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2014
• Subjects: Computer Science; Datavetenskap; Deep learning; Time-series; Unsupervised feature learning; Unsupervised feature learning; Deep learning; Time-series
• ISSN: 0167-8655; 1872-7344; 1872-7344
• DOI: 10.1016/j.patrec.2014.01.008

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.