A Multi-Seasonal SS-MSTL-DR Approach With Efficient Training Using Deep Learning and LLaMA: A Case Study of 6-Element Air Quality Prediction in a Subtropical City
Large models and deep learning models for real-time services are trained from time to time to refresh the new features and achieve better accuracy. Multivariate time-series decomposition and recombination (MTS-DR) models effectively improve training efficiency. In this research, an MTS method that i...
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| 主要な著者: | , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
IEEE
2025-01-01
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| シリーズ: | IEEE Access |
| 主題: | |
| オンライン･アクセス: | https://ieeexplore.ieee.org/document/11184121/ |
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