Projects per year
Abstract
In the last five years there have been a large number of new time series classification algorithms proposed in the literature. These algorithms have been evaluated on subsets of the 47 data sets in the University of California, Riverside time series classification archive. The archive has recently been expanded to 85 data sets, over half of which have been donated by researchers at the University of East Anglia. Aspects of previous evaluations have made comparisons between algorithms difficult. For example, several different programming languages have been used, experiments involved a single train/test split and some used normalised data whilst others did not. The relaunch of the archive provides a timely opportunity to thoroughly evaluate algorithms on a larger number of datasets. We have implemented 18 recently proposed algorithms in a common Java framework and compared them against two standard benchmark classifiers (and each other) by performing 100 resampling experiments on each of the 85 datasets. We use these results to test several hypotheses relating to whether the algorithms are significantly more accurate than the benchmarks and each other. Our results indicate that only 9 of these algorithms are significantly more accurate than both benchmarks and that one classifier, the Collective of Transformation Ensembles, is significantly more accurate than all of the others. All of our experiments and results are reproducible: we release all of our code, results and experimental details and we hope these experiments form the basis for more rigorous testing of new algorithms in the future.
Original language | English |
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Pages (from-to) | 606–660 |
Number of pages | 55 |
Journal | Data Mining and Knowledge Discovery |
Volume | 31 |
Issue number | 3 |
Early online date | 23 Nov 2016 |
DOIs | |
Publication status | Published - May 2017 |
Profiles
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Jason Lines
- School of Computing Sciences - Associate Professor in Computing Sciences
- Data Science and AI - Member
- Smart Emerging Technologies - Member
Person: Research Group Member, Academic, Teaching & Research
Projects
- 1 Finished
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The Collective of Transform Ensembles (COTE) for Time Series Classification
Engineering and Physical Sciences Research Council
1/05/15 → 31/10/18
Project: Research
Research output
- 1031 Citations (Scopus)
- 1 Conference contribution
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Binary Shapelet Transform for Multiclass Time Series Classification
Bostrom, A. & Bagnall, A., 1 Sept 2015, Big Data Analytics and Knowledge Discovery: 17th International Conference, DaWaK 2015, Valencia, Spain, September 1-4, 2015, Proceedings. Madria, S. & Hara, T. (eds.). Springer, Vol. 9263. p. 257-269 (Lecture Notes in Computer Science; vol. 9263).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile51 Citations (SciVal)94 Downloads (Pure)