This PhD project is offered on a self-funding basis. It is open to applicants with funding or those applying to funding sources. Details of tuition fees can be found at View Website.
A bench fee is also payable on top of the tuition fee to cover specialist equipment or laboratory costs required for the research. Applicants should contact the primary supervisor for further information about the fee associated with the project.
i) 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. https://link.springer.com/article/10.1007/s10618-018-0565-y
ii) 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.
iii) 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
iv) 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
v) 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