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A review of unsupervised feature learning and deep learning for time-series modeling
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-0458-2146
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-3122-693X
2014 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 42, no 1, 11-24 p.Article, review/survey (Refereed) Published
Abstract [en]

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.
Keyword [en]
Time-series, Unsupervised feature learning, Deep learning
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-34597DOI: 10.1016/j.patrec.2014.01.008ISI: 000333451300002Scopus ID: 2-s2.0-84894359867OAI: oai:DiVA.org:oru-34597DiVA: diva2:710518
Available from: 2014-04-07 Created: 2014-04-07 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Modeling time-series with deep networks
Open this publication in new window or tab >>Modeling time-series with deep networks
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Örebro: Örebro university, 2014. 56 p.
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 63
Keyword
multivariate time-series, deep learning, representation learning, unsupervised
National Category
Computer and Information Science
Research subject
Information technology
Identifiers
urn:nbn:se:oru:diva-39415 (URN)978-91-7529-054-6 (ISBN)
Public defence
2015-02-02, Hörsalen, Musikhögskolan, Örebro universitet, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2014-12-08 Created: 2014-12-08 Last updated: 2017-10-17Bibliographically approved

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Längkvist, MartinKarlsson, LarsLoutfi, Amy

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