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Time Series Data Mining (BAGNALLU16SF)

This project is no longer listed in the FindAPhD
database and may not be available.

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for PhD studentship opportunities
  • Full or part time
    Dr Bagnall
  • Application Deadline
    No more applications being accepted
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Dr. Bagnall’s main research interest is in developing techniques for time series data mining and applying them to a wide range of problem domains. He would welcome applications from students who have secured their own funding who wish to work in any areas of time series data mining. Specifically, Dr. Bagnall has PhD projects available in Time Series Classification and is looking for people to work on image outline classification and with environmental datasets, food sciences data, smart meter data and motion capture, but he will consider proposals in related areas such as time clustering, regression and rule induction and other problem domains.

If you would like to discuss a PhD research project in more detail, please contact Dr. Bagnall directly ([email protected]).

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 http://www.uea.ac.uk/pgresearch/pgrfees.

A bench fee is also payable on top of the tuition fee to cover specialist equipment or laboratory costs required for the research. The amount charged annually will vary considerably depending on the nature of the project and applicants should contact the primary supervisor for further information about the fee associated with the project.

References

i) Bagnall, A., Lines, J., Hills, J. and Bostrom, A. (2015) Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles. IEEE Transactions on Knowledge and Data Engineering, online first
ii) Lines, J. and Bagnall, A. (2015) Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery Journal, 29 (3). pp. 565-592
iii) Hills, J., Lines, J, Baranauskas, E., Mapp, J. and Bagnall, A. (2014) Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery Journal, 28 (4). pp. 851-881
iv) Bagnall, Anthony and Janacek, Gareth (2014) A Run Length Transformation for Discriminating Between Auto Regressive Time Series. Journal of Classification, 31 (2). pp. 154-178
v) Bagnall, AJ, Ratanamahatan, C, Keogh, E, Lonardi, S and Janacek, GJ (2006) A Bit Level Representation for Time Series Data Mining with Shape Based Similarity. Data Mining and Knowledge Discovery Journal, 13 (1). pp. 11-40

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