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Anomaly Detection in Time Series (BAGNALLAU20SCIEP)

Project Description

Time series classification (TSC) involves building predictive models for problems where the attributes are ordered. So, for example, we may want to predict whether a patient is healthy or not based on their heart beat. UEA has been at the forefront in developing algorithms for this type of problem [1,2,3]. TSC assumes that each observation is independent (e.g. a separate patient). An alternative common scenario is when we have a time series where readings are continuously taken, and we wish to detect when a specific event occurs. We may, for example, want to detect when a person falls over based on a motion sensor that is continuously collecting data. There are a range of techniques for doing this that are closely related to classification algorithms. The student will work in a vibrant research group to design algorithms within open source code frameworks [4,5] and evaluate them on a range of real world problems. There will be opportunities to work with our collaborators across the world, including researchers at the Alan Turing Institute, the University of California, USA and Monash University, Australia.

For more information on the supervisor for this project, please go here:

This is a PhD programme.

The start date of the project is 1 October 2020.

The mode of study is full-time. The studentship length is 3.5 years.

Entry requirements:

Acceptable first degree in Computer Science or a Computer Science related subject.

The standard minimum entry requirement is 1st or a Masters.

Funding Notes

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.


[1] 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.

[2] 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.

[3] 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

[4] 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

[5] 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

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