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Tony Bagnall
Tony Bagnall
Professor of Computer Science, University of Southampton
Verified email at uea.ac.uk - Homepage
Title
Cited by
Cited by
Year
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
A Bagnall, J Lines, A Bostrom, J Large, E Keogh
Data mining and knowledge discovery 31, 606-660, 2017
17102017
The UCR time series classification archive
Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista
July, 2015
10202015
The UCR time series archive
HA Dau, A Bagnall, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ...
IEEE/CAA Journal of Automatica Sinica 6 (6), 1293-1305, 2019
8422019
Classification of time series by shapelet transformation
J Hills, J Lines, E Baranauskas, J Mapp, A Bagnall
Data mining and knowledge discovery 28, 851-881, 2014
6282014
Time series classification with ensembles of elastic distance measures
J Lines, A Bagnall
Data Mining and Knowledge Discovery 29, 565-592, 2015
6062015
Time-series classification with COTE: the collective of transformation-based ensembles
A Bagnall, J Lines, J Hills, A Bostrom
IEEE Transactions on Knowledge and Data Engineering 27 (9), 2522-2535, 2015
5692015
A shapelet transform for time series classification
J Lines, LM Davis, J Hills, A Bagnall
Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012
4662012
The next release problem
AJ Bagnall, VJ Rayward-Smith, IM Whittley
Information and software technology 43 (14), 883-890, 2001
4632001
The UEA multivariate time series classification archive, 2018
A Bagnall, HA Dau, J Lines, M Flynn, J Large, A Bostrom, P Southam, ...
arXiv preprint arXiv:1811.00075, 2018
3992018
Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles
J Lines, S Taylor, A Bagnall
ACM Transactions on Knowledge Discovery from Data (TKDD) 12 (5), 1-35, 2018
3792018
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
AP Ruiz, M Flynn, J Large, M Middlehurst, A Bagnall
Data Mining and Knowledge Discovery 35 (2), 401-449, 2021
3622021
The UCR time series classification archive
HA Dau, E Keogh, K Kamgar, CCM Yeh, Y Zhu, S Gharghabi, ...
URL https://www. cs. ucr. edu/~ eamonn/time_series_data_2018, 2018
2682018
sktime: A unified interface for machine learning with time series
M Löning, A Bagnall, S Ganesh, V Kazakov, J Lines, FJ Király
arXiv preprint arXiv:1909.07872, 2019
2632019
Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification
J Lines, S Taylor, A Bagnall
2016 IEEE 16th international conference on data mining (ICDM), 1041-1046, 2016
2352016
A novel bit level time series representation with implication of similarity search and clustering
C Ratanamahatana, E Keogh, AJ Bagnall, S Lonardi
Advances in Knowledge Discovery and Data Mining: 9th Pacific-Asia Conference …, 2005
1922005
HIVE-COTE 2.0: a new meta ensemble for time series classification
M Middlehurst, J Large, M Flynn, J Lines, A Bostrom, A Bagnall
Machine Learning 110 (11), 3211-3243, 2021
1852021
Transformation based ensembles for time series classification
A Bagnall, L Davis, J Hills, J Lines
Proceedings of the 2012 SIAM international conference on data mining, 307-318, 2012
1642012
A multiagent model of the UK market in electricity generation
AJ Bagnall, GD Smith
IEEE Transactions on Evolutionary Computation 9 (5), 522-536, 2005
1462005
The UEA & UCR time series classification repository
A Bagnall, J Lines, W Vickers, E Keogh
URL http://www. timeseriesclassification. com 122, 2018
1442018
Clustering time series with clipped data
A Bagnall, G Janacek
Machine learning 58, 151-178, 2005
1442005
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