Time series classification with ensembles of elastic distance measures
- Submitting institution
-
The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 182619245
- Type
- D - Journal article
- DOI
-
10.1007/s10618-014-0361-2
- Title of journal
- Data Mining and Knowledge Discovery
- Article number
- -
- First page
- 565
- Volume
- 29
- Issue
- 3
- ISSN
- 1384-5810
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
-
- Citation count
- 134
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is an extension of a paper in the 2014 SIAM International Conference on Data Mining (approx. 10% acceptance). The proposed method (EE) was the new state-of-the-art approach in time series classification and its development directly contributed to a successful EPSRC responsive mode grant (EP/M015087/1) by forming part of the headline COTE algorithm. The design fundamentals from EE have been developed further in subsequent algorithms such as HIVE-COTE at UEA, and Fast EE and Proximity Forest from Monash University. EE is included in sktime, an open-source time series toolkit collaboration between researchers from the Alan Turing Institute and UEA.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -