| 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 | 494 | 2021 |
| 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 | 289 | 2021 |
| The Canonical Interval Forest (CIF) Classifier for Time Series Classification M Middlehurst, J Large, A Bagnall IEEE International Conference on Big Data, 2020 | 151 | 2020 |
| Bake off redux: a review and experimental evaluation of recent time series classification algorithms M Middlehurst, P Schäfer, A Bagnall Data Mining and Knowledge Discovery, 1-74, 2024 | 120 | 2024 |
| Scalable dictionary classifiers for time series classification M Middlehurst, W Vickers, A Bagnall Intelligent Data Engineering and Automated Learning–IDEAL 2019: 20th …, 2019 | 115 | 2019 |
| The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification M Middlehurst, J Large, G Cawley, A Bagnall The European Conference on Machine Learning and Principles and Practice of …, 2020 | 107 | 2020 |
| On the Usage and Performance of The Hierarchical Vote Collective of Transformation-based Ensembles version 1.0 (HIVE-COTE 1.0) A Bagnall, M Flynn, J Large, J Lines, M Middlehurst International Workshop on Advanced Analytics and Learning on Temporal Data, 2020 | 95* | 2020 |
| A review and evaluation of elastic distance functions for time series clustering C Holder, M Middlehurst, A Bagnall Knowledge and Information Systems 66 (2), 765-809, 2024 | 47 | 2024 |
| The FreshPRINCE: A simple transformation based pipeline time series classifier M Middlehurst, A Bagnall Pattern Recognition and Artificial Intelligence: Third International …, 2022 | 21 | 2022 |
| alan-turing-institute/sktime: v0. 10.1 M Löning, T Bagnall, M Middlehurst, F Király, S Ganesh, G Oastler, ... Zenodo, 2022 | 21* | 2022 |
| Unsupervised feature based algorithms for time series extrinsic regression D Guijo-Rubio, M Middlehurst, G Arcencio, DF Silva, A Bagnall Data Mining and Knowledge Discovery, 1-45, 2024 | 15 | 2024 |
| aeon: a Python toolkit for learning from time series M Middlehurst, A Ismail-Fawaz, A Guillaume, C Holder, D Guijo-Rubio, ... Journal of Machine Learning Research 25 (289), 1-10, 2024 | 9 | 2024 |
| A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency A Bagnall, F Király, M Löning, M Middlehurst, G Oastler arXiv preprint arXiv:1909.05738, 2019 | 9 | 2019 |
| Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression M Middlehurst, A Bagnall International Workshop on Advanced Analytics and Learning on Temporal Data …, 2023 | 4 | 2023 |
| A Hands-on Introduction to Time Series Classification and Regression A Bagnall, M Middlehurst, G Forestier, A Ismail-Fawaz, A Guillaume, ... Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 2 | 2024 |
| A tale of two toolkits, report the second: bake off redux. Chapter 1. dictionary based classifiers A Bagnall, J Large, M Middlehurst arXiv preprint arXiv:1911.12008, 2019 | 2 | 2019 |
| Detection of nuisance call centres using improved hybrid time series classification algorithms M Middlehurst University of East Anglia, 2023 | | 2023 |
Tony BagnallProfessor of Computer Science, University of SouthamptonVerified email at uea.ac.uk