The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Deep Neural Network Ensembles for Time Series Classification
Open accessAbstract
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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“we find that classification performance of a single IE model shows high standard deviation due to the random weight initialization in the experiment [68].”
From IENet: a robust convolutional neural network for EEG based brain-computer interfaces (2022) · cites this paper as [68]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
(2014) · Cited by 33,419
(2010) · Cited by 14,134
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,051
Deep learning for time series classification: a review
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342 · cited 10×
“All of these architectures were implemented in a common framework during our empirical study [19], containing originally 9 different deep learning approaches for TSC.”
BackgroundMethodInfluentialSelf-citationTime series classification from scratch with deep neural networks: A strong baseline
Wang, Z., Yan, W., & Oates, T. (2017) · 2017 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966039 · Cited by 2,299
Bagnall, A., Lines, J., Bostrom, A., Large, J., & Keogh, E. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0483-9 · Cited by 1,377
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Looking Around You: External Information Enhances Representations for Event Sequences
Sokerin, P., Kovaleva, M., Boyarina, E., Tikhomirov, P., Vorobiyov, D., & Zaytsev, A. A. (2026) · IEEE Access · doi:10.1109/access.2026.3695159
Towards Early Crop Classification Using Deep Learning
Oumoussa, S., & Peluffo-Ordóñez, D. H. (2026) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-032-18927-1_9
Metadata
- 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)
- Venue
- 2019 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2019-07
- Pages
- 1-6
- ISSN
- 2161-4407 (ISSN-L)
- 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
- 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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