Time series classification with ensembles of elastic distance measures

Lines, Jason ORCID: https://orcid.org/0000-0002-1496-5941 and Bagnall, Anthony (2015) Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery, 29 (3). pp. 565-592. ISSN 1384-5810

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Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series classification (TSC) problems. These include variants of dynamic time warping (DTW), such as weighted and derivative DTW, and edit distance-based measures, including longest common subsequence, edit distance with real penalty, time warp with edit, and move–split–merge. These measures have the common characteristic that they operate in the time domain and compensate for potential localised misalignment through some elastic adjustment. Our aim is to experimentally test two hypotheses related to these distance measures. Firstly, we test whether there is any significant difference in accuracy for TSC problems between nearest neighbour classifiers using these distance measures. Secondly, we test whether combining these elastic distance measures through simple ensemble schemes gives significantly better accuracy. We test these hypotheses by carrying out one of the largest experimental studies ever conducted into time series classification. Our first key finding is that there is no significant difference between the elastic distance measures in terms of classification accuracy on our data sets. Our second finding, and the major contribution of this work, is to define an ensemble classifier that significantly outperforms the individual classifiers. We also demonstrate that the ensemble is more accurate than approaches not based in the time domain. Nearly all TSC papers in the data mining literature cite DTW (with warping window set through cross validation) as the benchmark for comparison. We believe that our ensemble is the first ever classifier to significantly outperform DTW and as such raises the bar for future work in this area.

Item Type: Article
Uncontrolled Keywords: time series classification,elastic distance measure,ensemble,artificial intelligence ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Science
UEA Research Groups: Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Science > Research Groups > Smart Emerging Technologies
Depositing User: Pure Connector
Date Deposited: 25 Jul 2014 15:20
Last Modified: 20 Oct 2022 19:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/49613
DOI: 10.1007/s10618-014-0361-2

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