On the Usage and Performance of the Hierarchical Vote Collective of Transformation-Based Ensembles Version 1.0 (HIVE-COTE v1.0)

Tony Bagnall, Michael Flynn, James Large, Matthew Middlehurst

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

31 Citations (Scopus)


The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. Since it was first proposed in 2016, the algorithm has undergone some minor changes and there is now a configurable, scalable and easy to use version available in two open source repositories. We present an overview of the latest stable HIVE-COTE, version 1.0, and describe how it differs to the original. We provide a walkthrough guide of how to use the classifier, and conduct extensive experimental evaluation of its predictive performance and resource usage. We compare the performance of HIVE-COTE to three recently proposed algorithms.

Original languageEnglish
Title of host publication Lecture Notes in Computer Science
Subtitle of host publication Advanced Analytics and Learning on Temporal Data (AALTD)
EditorsVincent Lemaire, Simon Malinowski, Anthony Bagnall, Thomas Guyet, Romain Tavenard, Georgiana Ifrim
Number of pages16
ISBN (Electronic)978-3-030-65742-0
ISBN (Print)978-3-030-65741-3
Publication statusPublished - 16 Dec 2020


  • Classification
  • Heterogeneous ensembles
  • Time series

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