The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification
Matthew Middlehurst, James Large, Gavin Cawley, Anthony Bagnall

TL;DR
This paper introduces the Temporal Dictionary Ensemble (TDE), a novel time series classifier that outperforms existing dictionary-based methods and enhances the state-of-the-art ensemble HIVE-COTE, surpassing deep learning approaches.
Contribution
The paper proposes TDE, a new dictionary-based classifier with an adaptive Gaussian process ensemble, significantly improving accuracy over previous methods and the HIVE-COTE ensemble.
Findings
TDE outperforms other dictionary-based classifiers in accuracy.
Replacing BOSS with TDE in HIVE-COTE yields significant accuracy improvements.
HIVE-COTE with TDE surpasses current deep learning and other state-of-the-art classifiers.
Abstract
Using bag of words representations of time series is a popular approach to time series classification. 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…
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Taxonomy
MethodsGaussian Process
