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Time Series Classification at Scale (BAGNALLAU20SF)

  • Full or part time
  • Application Deadline
    Sunday, May 31, 2020
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

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]. This PhD project will focus on developing algorithms for TSC for large datasets. Recent advances have shown that state of the art accuracy can be obtained from high levels of randomization. This project will involve exploring how to get the most accurate system for large data given time and memory constraints, and the trade of between accuracy and time. The PhD 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 accross the world, including researchers at the Alan Turing Institute, the University of California, USA and Monash University, Australia.

More information on the supervisor for this project: https://people.uea.ac.uk/anthony_bagnall

This is a PhD programme.

The start date of the project is October 2020.

The mode of study is full-time.

Entry requirements:
1st or Masters in Computer Science or related discipline

Funding Notes

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.

References

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.
https://dl.acm.org/citation.cfm?id=3182382
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
https://link.springer.com/article/10.1007/s10618-016-0483-9
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
https://github.com/alan-turing-institute/sktime
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
https:// https://github.com/uea-machine-learning/tsml

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