Cited by 333Time Series Analysis and ForecastingMusic and Audio ProcessingImage Retrieval and Classification Techniques

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Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles

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Lines, Jason, Taylor, Sarah, Bagnall, Anthony·2018·ACM Transactions on Knowledge Discovery from Data

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Abstract

A recent experimental evaluation assessed 19 time series classification (TSC) algorithms and found that one was significantly more accurate than all others: the Flat Collective of Transformation-based Ensembles (Flat-COTE). Flat-COTE is an ensemble that combines 35 classifiers over four data representations. However, while comprehensive, the evaluation did not consider deep learning approaches. Convolutional neural networks (CNN) have seen a surge in popularity and are now state of the art in many fields and raises the question of whether CNNs could be equally transformative for TSC. We implement a benchmark CNN for TSC using a common structure and use results from a TSC-specific CNN from the literature. We compare both to Flat-COTE and find that the collective is significantly more accurate than both CNNs. These results are impressive, but Flat-COTE is not without deficiencies. We significantly improve the collective by proposing a new hierarchical structure with probabilistic voting, defining and including two novel ensemble classifiers built in existing feature spaces, and adding further modules to represent two additional transformation domains. The resulting classifier, the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE), encapsulates classifiers built on five data representations. We demonstrate that HIVE-COTE is significantly more accurate than Flat-COTE (and all other TSC algorithms that we are aware of) over 100 resamples of 85 TSC problems and is the new state of the art for TSC. Further analysis is included through the introduction and evaluation of 3 new case studies and extensive experimentation on 1,000 simulated datasets of 5 different types.

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Authors: Lines, J., Taylor, S., & Bagnall, A.
Affiliations: University of East Anglia (GB)
ORCID: Lines, Jason
Taylor, Sarah
Bagnall, Anthony
Venue: ACM Transactions on Knowledge Discovery from Data
Publisher: Association for Computing Machinery (ACM)
Published: 2018-07-05
Volume: 12
Issue: 5
Pages: 1-35
ISSN: 1556-4681 (ISSN-L)
1556-472X
DOI: 10.1145/3182382
OpenAlex: W2786161686
Cited by: 333
References: 48
Citations by year: 2026: 19 · 2025: 42 · 2024: 59 · 2023: 48 · 2022: 45 · 2021: 50 · 2020: 42 · 2019: 27 · 2018: 1
FWCI: 21.48
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: ACM Transactions on Knowledge Discovery from Data (publishedVersion, cc-by)
UEA Digital Repository (University of East Anglia) (submittedVersion)
Funders: UK Engineering and Physical Sciences Research Council (EP/M015807/1 and EP/M014053/1)
Research and Specialist Computing Support service at the University of East Anglia
Topics: Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords: time series classification, ensemble classifiers, convolutional neural networks, probabilistic voting, data representation, benchmark comparison

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Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles

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