Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles
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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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The TSC community lacks an overview of the current performance of deep learning models for solving the TSC problem.
“This shows how much the community lacks of an overview of the current performance of deep learning models for solving the TSC problem (Lines et al., 2018).”
From Deep learning for time series classification: a review (2019) · cites this paper as Lines et al., 2018
Cliques formed by the Wilcoxon Signed Rank Test with Holm's alpha correction do not necessarily reflect the rank order.
“It is worthwhile noting that cliques formed by the Wilcoxon Signed Rank Test with Holm's alpha correction do not necessary reflect the rank order (Lines et al., 2018).”
From Deep learning for time series classification: a review (2019) · cites this paper as Lines et al., 2018
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Time Series Classification using Fuzzy Cognitive Maps
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Broad fuzzy cognitive map systems for time series classification
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- Authors
- Lines, J., Taylor, S., & Bagnall, A.
- Affiliations
- University of East Anglia (GB)
- Venue
- ACM Transactions on Knowledge Discovery from Data
- Published
- 2018-07-05
- Volume
- 12
- Issue
- 5
- Pages
- 1-35
- ISSN
- 1556-4681 (ISSN-L)
- 1556-472X
- 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
- 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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