A review of unsupervised feature learning and deep learning for time-series modeling
2014 (English)In: Pattern Recognition Letters, ISSN 0167-8655, Vol. 42, no 1, 11-24 p.Article, review/survey (Refereed) Published
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.
Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 42, no 1, 11-24 p.
Time-series, Unsupervised feature learning, Deep learning
Research subject Computer Science
IdentifiersURN: urn:nbn:se:oru:diva-34597DOI: 10.1016/j.patrec.2014.01.008ISI: 000333451300002ScopusID: 2-s2.0-84894359867OAI: oai:DiVA.org:oru-34597DiVA: diva2:710518