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Middlehurst, Matthew, Large, James, Cawley, Gavin 
ORCID: https://orcid.org/0000-0002-4118-9095 and Bagnall, Anthony
(2021)
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification.
In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020-09-14 - 2020-09-18.
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Abstract
Using bag of words representations of time series is a popular approach to time series classification (TSC). These algorithms involve approximating and discretising windows over a series to form words, then forming a count of words over a given dictionary. Classifiers are constructed on the resulting histograms of word counts. A 2017 evaluation of a range of time series classifiers found the bag of symbolic-Fourier approximation symbols (BOSS) ensemble the best of the dictionary based classifiers. It forms one of the components of hierarchical vote collective of transformation-based ensembles (HIVE-COTE), which represents the current state of the art. Since then, several new dictionary based algorithms have been proposed that are more accurate or more scalable (or both) than BOSS. We propose a further extension of these dictionary based classifiers that combines the best elements of the others combined with a novel approach to constructing ensemble members based on an adaptive Gaussian process model of the parameter space. We demonstrate that the Temporal Dictionary Ensemble (TDE) is more accurate than other dictionary based approaches. Furthermore, unlike the other classifiers, if we replace BOSS in HIVE-COTE with TDE, HIVE-COTE becomes significantly more accurate. We also show this new version of HIVE-COTE is significantly more accurate than the current top performing classifiers on the UCR time series archive. This advance represents a new state of the art for time series classification.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | bag of words,classification,hive-cote,time series,theoretical computer science,computer science(all) ,/dk/atira/pure/subjectarea/asjc/2600/2614 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Science > Research Groups > Computational Biology |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 08 Jun 2020 23:29 |
| Last Modified: | 08 Mar 2024 00:59 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/75490 |
| DOI: | 10.1007/978-3-030-67658-2_38 |
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