Time series classification from scratch with deep neural networks: A strong baseline
Open accessAbstract
We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting. The proposed Fully Convolutional Network (FCN) achieves premium performance to other state-of-the-art approaches and our exploration of the very deep neural networks with the ResNet structure is also competitive. The global average pooling in our convolutional model enables the exploitation of the Class Activation Map (CAM) to find out the contributing region in the raw data for the specific labels. Our models provides a simple choice for the real world application and a good starting point for the future research. An overall analysis is provided to discuss the generalization capability of our models, learned features, network structures and the classification semantics.
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ResNet has strong representational capability and computational efficiency.
“we adopt ResNet [38] as the backbone encoder owing to its strong representational capability and computational efficiency.”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [38]
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- Authors
- Wang, Z., Yan, W., & Oates, T.
- Affiliations
- General Electric (United States) (US)
- Venue
- 2017 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 1578-1585
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2551393996
- Cited by
- 2,299
- References
- 20
- Citations by year
- 2026: 212 · 2025: 338 · 2024: 363 · 2023: 335 · 2022: 324 · 2021: 295 · 2020: 236 · 2019: 138 · 2018: 49 · 2017: 8
- FWCI
- 83.69
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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, end-to-end deep neural network, fully convolutional network, ResNet, global average pooling, class activation maps, representation learning, model generalization
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