The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Parallel spatio-temporal attention-based TCN for multivariate time series prediction
Open accessAbstract
As industrial systems become more complex and monitoring sensors for everything from surveillance to our health become more ubiquitous, multivariate time series prediction is taking an important place in the smooth-running of our society. A recurrent neural network with attention to help extend the prediction windows is the current-state-of-the-art for this task. However, we argue that their vanishing gradients, short memories, and serial architecture make RNNs fundamentally unsuited to long-horizon forecasting with complex data. Temporal convolutional networks (TCNs) do not suffer from gradient problems and they support parallel calculations, making them a more appropriate choice. Additionally, they have longer memories than RNNs, albeit with some instability and efficiency problems. Hence, we propose a framework, called PSTA-TCN, that combines a parallel spatio-temporal attention mechanism to extract dynamic internal correlations with stacked TCN backbones to extract features from different window sizes. The framework makes full use parallel calculations to dramatically reduce training times, while substantially increasing accuracy with stable prediction windows up to 13 times longer than the status quo.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Learning long-term dependencies with gradient descent is difficult
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Deep learning and its applications to machine health monitoring
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Cited by
Saheed, Y. K., Omole, A. I., & Sabit, M. O. (2025) · Sensors International · doi:10.1016/j.sintl.2024.100297 · Cited by 88
Metadata
- Authors
- Fan, J., Zhang, K., Huang, Y., Zhu, Y., & Chen, B.
- Venue
- Neural Computing and Applications
- Published
- 2021-05-12
- Volume
- 35
- Issue
- 18
- Pages
- 13109-13118
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W3160936371
- Cited by
- 440
- References
- 41
- FWCI
- 18.63
- Type
- journal article
- Language
- en
- Open access
- green
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