The Collective of Transform Ensembles (COTE) for Time Series Classification

Bagnall, Tony (Principal Investigator)

Status	Finished
Effective start/end date	1/05/15 → 31/10/18

Funding

Engineering and Physical Sciences Research Council: £317,804.00

5 Article
1 Conference contribution

A probabilistic classifier ensemble weighting scheme based on cross-validated accuracy estimates
Large, J., Lines, J. & Bagnall, A., 1 Nov 2019, In: Data Mining and Knowledge Discovery. 33, 6, p. 1674-1709 36 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File

37 Citations (Scopus)

47 Downloads (Pure)
On time series classification with dictionary-based classifiers
Large, J., Bagnall, A., Malinowski, S. & Tavenard, R., 24 Oct 2019, In: Intelligent Data Analysis. 23, 5, p. 1073-1089 17 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File

19 Citations (Scopus)

43 Downloads (Pure)
Optimizing dynamic time warping’s window width for time series data mining applications
Dau, H. A., Silva, D. F., Petitjean, F., Forestier, G., Bagnall, A., Mueen, A. & Keogh, E., Jul 2018, In: Data Mining and Knowledge Discovery. 32, 4, p. 1074–1120 27 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File

46 Citations (Scopus)

9 Downloads (Pure)