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Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China
OBJECTIVE: The purpose of this study is to develop a novel data-driven hybrid model by fusing ensemble empirical mode decomposition (EEMD), seasonal autoregressive integrated moving average (SARIMA), with nonlinear autoregressive artificial neural network (NARNN), called EEMD-ARIMA-NARNN model, to a...
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| Publicat a: | Infect Drug Resist |
|---|---|
| Autors principals: | , , , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Dove
2021
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8164697/ https://ncbi.nlm.nih.gov/pubmed/34079304 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.2147/IDR.S299704 |
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