The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification

Research output: Contribution to conferencePaper

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.

Original languageEnglish
Pages660-676
Number of pages17
DOIs
Publication statusPublished - 25 Feb 2021
EventThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Ghent, Belgium
Duration: 14 Sep 202018 Sep 2020
https://ecmlpkdd2020.net/

Conference

ConferenceThe European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated titleECML PKDD
CountryBelgium
CityGhent
Period14/09/2018/09/20
Internet address

Keywords

  • Bag of words
  • Classification
  • HIVE-COTE
  • Time series

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