Projects per year
Tony joined CMP as a part time MRes student/part time teaching assistant in 1993 having finished his BSc in Mathematics and Statistics at the University of Hertfordshire. After completing his Masters by research in 1995 he began a PhD titled "Modelling the UK electricity market with autonomous adaptive agents". After a brief period as a research assistant on a data mining project sponsored by Master Foods and Centrica, in 1999 he completed his PhD and was appointed as a Lecturer in Statistics for Data Mining jointly in the schools of Computer Science and Mathematics. In 2003 statistics moved into CMP and since then Tony has been a full member of the school. In 2007 he was promoted to senior lecturer.
Tony has been involved in researching areas of optimization, machine learning, agent systems, statistics and data mining. Currently he is primarily focused on time series data mining. He is involved in several ongoing projects, links to which can be found below:
Dr. Bagnall 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 projects available in Time Series Classification and is looking for people to work on image outline classification and with environmental datasets, 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 research projects in more detail, please contact Dr. Bagnall directly (firstname.lastname@example.org).
Key Research Interests and Expertise
His main current research interest is in the design and evaluation of algorithms for time series data mining and the development of novel time series application areas.
Selected Recent Publications:
Time series classification with ensembles of elastic distance measures
Bagnall, A. 2014 In : Data Mining and Knowledge Discovery Journal, online first
Classification of time series by shapelet transformation
Hills, J., Lines, J., Baranauskas, E., Mapp, J. & Bagnall, A. Jul 2014 In : Data Mining and Knowledge Discovery Journal. 28, 4, p. 851-881
A Run Length Transformation for Discriminating Between Auto Regressive Time Series Bagnall, A. & Janacek, G. Jun 2014 In : Journal of Classification. 31, 2, p. 154-178
Chairman of Examiners (undergraduate)
Course Director for Computing Science, Master of Computing Science and Computing Science with a year in industry
Deputy Teaching Director for CMP (undergraduate)
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Inflammatory drivers of long-term comorbidity trajectories: an AI investigation of multimorbidity (INFLAIM)
4/01/21 → 8/10/21
Mackiewicz, M., Bagnall, T., Day, A., De La Iglesia, B., Milner, B., Parr, G., Ren, E., Cawley, G., Finlayson, G., Huber, K., Lettice, F., Aung, M. H., Harvey, R., Buckley, O., Laycock, S., Lines, J., Kulinskaya, E., Moulton, V. & Wang, W.
10/01/20 → 9/07/21
1/11/18 → 30/06/19
1/05/18 → 31/10/18
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advancesPasos Ruiz, A., Flynn, M., Large, J., Middlehurst, M. & Bagnall, A., Mar 2021, In : Data Mining and Knowledge Discovery. 35, p. 401–449 49 p.
Research output: Contribution to journal › ArticleOpen AccessFile1 Citation (Scopus)15 Downloads (Pure)
Middlehurst, M., Large, J., Cawley, G. & Bagnall, A., 25 Feb 2021, p. 660-676. 17 p.
Research output: Contribution to conference › Paper
An Ultra-Fast Time Series Distance Measure to allow Data Mining in more Complex Real-World DeploymentsGharghabi, S., Imani, S., Bagnall, A., Darvishzadeh, A. & Keogh, E., Jul 2020, In : Data Mining and Knowledge Discovery. 34, 4, p. 1104–1135 32 p.
Research output: Contribution to journal › ArticleOpen AccessFile1 Citation (Scopus)2 Downloads (Pure)
Khampuengson, T., Bagnall, T. & Wang, W., 8 Dec 2020, The 22nd International Conference on Big Data Analytics and Knowledge Discovery. Bramer, M. & Ellis, R. (eds.). Springer, p. 145-151 7 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0)Bagnall, T., Flynn, M., Large, J. & Middlehurst, M., 16 Dec 2020, Lecture Notes in Computer Science: Advanced Analytics and Learning on Temporal Data (AALTD) . Lemaire, V., Malinowski, S., Bagnall, A., Guyet, T., Tavenard, R. & Ifrim, G. (eds.). Springer, Vol. 12588. p. 3-18 16 p.
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)1 Citation (Scopus)