This PhD project is in an EPSRC studentship competition within the Faculty of Science. Funding is for 3.5 years and will be available to successful candidates who meets the UK Research Council eligibility criteria. These requirements are detailed in the Research Council Training Grant Guide which can be found at View Website (see Annex 1 for Residential Guidelines for Research Council Studentships). In most cases UK and EU nationals who have been ordinarily resident in the UK for 3 years prior to the start of the course are eligible for a full-award. Other EU nationals may qualify for a fees-only award.
 Imani, S., Bagnall, A., Darvishzadeh, A. and Keogh, E. Matrix Profile XII: MPdist: A Novel Time Series Distance Measure to Allow Data Mining in More Challenging Scenarios, IEEE International Conference on Data Mining, 2018.
 Lines, J., Taylor, S. and Bagnall, A Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-based Ensembles. ACM Transactions on Knowledge Discovery from Data. 12(5): 52-87, 2018.
 Bagnall, A., Lines, J., Bostrom, A., Large, J. and Keogh, E. The Great Time Series Classification Bake Off: a Review and Experimental Evaluation of Recent Algorithmic Advances. Data Mining and Knowledge Discovery, 31(3): 606-660, 2017
 Löning, M. Bagnall, A., Ganesh, S., Kazakov, V., Lines, J and Király, F.. sktime: A Unified Interface for Machine Learning with Time Series, Workshop on ML for Systems at NeurIPS, 2019
 Bagnall, A., Király, F., Löning, M., Middlehurst, M. and Oastler, G. A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency ArXiv preprint, 2019