| 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 (3), 606-660, 2017 | 1055 | 2017 |
| The UCR time series classification archive Y Chen, E Keogh, B Hu, N Begum, A Bagnall, A Mueen, G Batista July, 2015 | 868 | 2015 |
| Classification of time series by shapelet transformation J Hills, J Lines, E Baranauskas, J Mapp, A Bagnall Data mining and knowledge discovery 28 (4), 851-881, 2014 | 470 | 2014 |
| The next release problem AJ Bagnall, VJ Rayward-Smith, IM Whittley Information and software technology 43 (14), 883-890, 2001 | 428 | 2001 |
| 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 | 421 | 2019 |
| Time series classification with ensembles of elastic distance measures J Lines, A Bagnall Data Mining and Knowledge Discovery 29 (3), 565-592, 2015 | 421 | 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 | 419 | 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 | 352 | 2012 |
| 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 12 (5), 2018 | 206 | 2018 |
| A novel bit level time series representation with implication of similarity search and clustering C Ratanamahatana, E Keogh, AJ Bagnall, S Lonardi Pacific-Asia conference on knowledge discovery and data mining, 771-777, 2005 | 165 | 2005 |
| 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 | 157 | 2018 |
| 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 | 141 | 2012 |
| A multiagent model of the UK market in electricity generation AJ Bagnall, GD Smith IEEE Transactions on Evolutionary Computation 9 (5), 522-536, 2005 | 141 | 2005 |
| 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 | 137 | 2016 |
| Clustering time series from ARMA models with clipped data AJ Bagnall, GJ Janacek Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 128 | 2004 |
| Clustering time series with clipped data A Bagnall, G Janacek Machine learning 58 (2), 151-178, 2005 | 127 | 2005 |
| 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 | 105 | 2019 |
| A bit level representation for time series data mining with shape based similarity A Bagnall, C Ratanamahatana, E Keogh, S Lonardi, G Janacek Data mining and knowledge discovery 13 (1), 11-40, 2006 | 104 | 2006 |
| The UEA & UCR time series classification repository A Bagnall, J Lines, W Vickers, E Keogh URL http://www. timeseriesclassification. com 122, 2018 | 93 | 2018 |
| Data mining rules using multi-objective evolutionary algorithms B De La Iglesia, MS Philpott, AJ Bagnall, VJ Rayward-Smith The 2003 Congress on Evolutionary Computation, 2003. CEC'03. 3, 1552-1559, 2003 | 77 | 2003 |
Jason LinesLecturer in Computer Science, University of East AngliaE-mail confirmado em uea.ac.uk
