Cited by 103Time Series Analysis and ForecastingMusic and Audio ProcessingNeural Networks and Applications

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Deep Neural Network Ensembles for Time Series Classification

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Ismail Fawaz, Hassan, Forestier, Germain, Weber, Jonathan, Idoumghar, Lhassane, Muller, Pierre-Alain·2019·2019 International Joint Conference on Neural Networks (IJCNN)

DOI 10.1109/ijcnn.2019.8852316

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Abstract

Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are currently composed of ensembles of 37 non deep learning based classifiers. We attribute this gap in performance due to the lack of neural network ensembles for TSC. Therefore in this paper, we show how an ensemble of 60 deep learning models can significantly improve upon the current state-of-the-art performance of neural networks for TSC, when evaluated over the UCR/UEA archive: the largest publicly available benchmark for time series analysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is the first time series classifier to outperform COTE while reaching similar performance to the current state-of-the-art ensemble HIVE-COTE.

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Authors: Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A.
Affiliations: IRIMAS, Université Haute-Alsace, Mulhouse, France
Université de Haute-Alsace (FR)
ORCID: Forestier, Germain
Weber, Jonathan
Idoumghar, Lhassane
Venue: 2019 International Joint Conference on Neural Networks (IJCNN)
Publisher: IEEE
Published: 2019-07
Pages: 1-6
ISSN: 2161-4407 (ISSN-L)
DOI: 10.1109/ijcnn.2019.8852316
OpenAlex: W2978259066
Cited by: 103
References: 47
Citations by year: 2026: 3 · 2025: 10 · 2024: 15 · 2023: 21 · 2022: 17 · 2021: 21 · 2020: 14 · 2019: 2
FWCI: 10.35
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
HAL (submittedVersion, other-oa)
arXiv (Cornell University)
Funders: Nvidia
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 › Artificial Intelligence › Neural Networks and Applications
Keywords: time series classification, neural network ensembles, deep neural network, ensemble learning, CotE, ensemble classifiers

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Deep Neural Network Ensembles for Time Series Classification

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