| 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 | 1803 | 2017 |
| The UCR time series classification archive Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista July, 2015 | 1043 | 2015 |
| 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 | 942 | 2019 |
| 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 | 655 | 2014 |
| Time series classification with ensembles of elastic distance measures J Lines, A Bagnall Data Mining and Knowledge Discovery 29, 565-592, 2015 | 638 | 2015 |
| 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 | 588 | 2015 |
| 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 | 483 | 2012 |
| 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 | 470 | 2018 |
| The next release problem AJ Bagnall, VJ Rayward-Smith, IM Whittley Information and software technology 43 (14), 883-890, 2001 | 470 | 2001 |
| 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 | 435 | 2021 |
| 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 | 405 | 2018 |
| 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 | 299 | 2019 |
| 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 | 278 | 2018 |
| 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 | 249 | 2016 |
| 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 | 236 | 2021 |
| 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 | 193 | 2005 |
| 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 | 167 | 2012 |
| The UEA & UCR time series classification repository A Bagnall, J Lines, W Vickers, E Keogh URL http://www. timeseriesclassification. com 122, 2018 | 146 | 2018 |
| A multiagent model of the UK market in electricity generation AJ Bagnall, GD Smith IEEE Transactions on Evolutionary Computation 9 (5), 522-536, 2005 | 146 | 2005 |
| Clustering time series with clipped data A Bagnall, G Janacek Machine learning 58, 151-178, 2005 | 145 | 2005 |
Jason LinesAssociate Professor in Computer Science, University of East AngliaGeverifieerd e-mailadres voor uea.ac.uk
