catch22: CAnonical Time-series CHaracteristics-Reference-Cited by-同舟云学术

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catch22: CAnonical Time-series CHaracteristics

Published:2019-08-09 Issue:6 Volume:33 Page:1821-1852
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Lubba Carl H.^ORCID,Sethi Sarab S.^ORCID,Knaute Philip,Schultz Simon R.^ORCID,Fulcher Ben D.^ORCID,Jones Nick S.^ORCID

Funder

Engineering and Physical Sciences Research Council

National Health and Medical Research Council

Natural Environment Research Council

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s10618-019-00647-x.pdf

Reference35 articles.

1. Bagnall A, Davis LM, Hills J, Lines J (2012) Transformation based ensembles for time series classification. In: Proceedings of the 2012 SIAM international conference on data mining, pp 307–318. ISBN 978-1-61197-232-0

2. Bagnall A, Lines J, Hills J, Bostrom A (2016) Time-series classification with COTE: the collective of transformation-based ensembles. In: 2016 IEEE 32nd international conference on data engineering, ICDE, vol 27, no 9, pp 1548–1549, 2016. ISSN 10414347. https://doi.org/10.1109/ICDE.2016.7498418

3. Bagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Discov 31(3):606–660. ISSN 1573756X. https://doi.org/10.1007/s10618-016-0483-9

4. Bagnall A, Lines J, Vickers W, Keogh E The UEA & UCR time series classification repository. http://www.timeseriesclassification.com/

5. Bandara K, Bergmeir C, Smyl S (2017) Forecasting across time series databases using long short-term memory networks on groups of similar series: a clustering approach. arXiv. ISSN 13578170. https://doi.org/10.1002/pdi.718 . http://arxiv.org/abs/1710.03222

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