期刊
PATTERN RECOGNITION LETTERS
卷 42, 期 -, 页码 11-24出版社
ELSEVIER
DOI: 10.1016/j.patrec.2014.01.008
关键词
Time-series; Unsupervised feature learning; Deep learning
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. (C) 2014 Elsevier B.V. All rights reserved.
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