HIVE-COTE 2.0: a new meta ensemble for time series classification

TL;DR

HIVE-COTE 2.0 is an improved ensemble method for time series classification that significantly outperforms previous state-of-the-art algorithms on multiple datasets.

Contribution

The paper introduces HIVE-COTE 2.0 with new classifiers and ensemble components, enhancing accuracy and usability over the original HIVE-COTE.

Findings

HIVE-COTE 2.0 outperforms previous algorithms on 112 UCR datasets.

HIVE-COTE 2.0 outperforms previous algorithms on 26 UEA datasets.

Introduction of two novel classifiers: TDE and DrCIF.

Abstract

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its ensemble from classifiers of multiple domains, including phase-independent shapelets, bag-of-words based dictionaries and phase-dependent intervals. Since it was first proposed in 2016, the algorithm has remained state of the art for accuracy on the UCR time series classification archive. Over time it has been incrementally updated, culminating in its current state, HIVE-COTE 1.0. During this time a number of algorithms have been proposed which match the accuracy of HIVE-COTE. We propose comprehensive changes to the HIVE-COTE algorithm which significantly improve its accuracy and usability, presenting this upgrade as HIVE-COTE 2.0. We introduce two novel classifiers, the Temporal Dictionary Ensemble (TDE) and Diverse Representation Canonical Interval Forest (DrCIF), which replace existing ensemble members. Additionally, we introduce the Arsenal, an ensemble of ROCKET classifiers as a new HIVE-COTE 2.0 constituent. We demonstrate that HIVE-COTE 2.0 is significantly more accurate than the current state of the art on 112 univariate UCR archive datasets and 26 multivariate UEA archive datasets.