MTS Decomposition and Recombining Significantly Improves Training Efficiency in Deep Learning: A Case Study in Air Quality Prediction over Sub-Tropical Area
It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better...
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| Główni autorzy: | , , |
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| Format: | Artigo |
| Język: | Inglês |
| Wydane: |
MDPI AG
2024-04-01
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| Seria: | Atmosphere |
| Hasła przedmiotowe: | |
| Dostęp online: | https://www.mdpi.com/2073-4433/15/5/521 |
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