Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
534413 | Pattern Recognition Letters | 2014 | 14 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Martin LÀngkvist, Lars Karlsson, Amy Loutfi,