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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Detalles Bibliográficos
Autores principales: Li, Jizhen, Li, Yuhong, Ye, Ming, Yao, Sanqiao, Yu, Chongchong, Wang, Lei, Wu, Weidong, Wang, Yongbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164697/
https://www.ncbi.nlm.nih.gov/pubmed/34079304
http://dx.doi.org/10.2147/IDR.S299704

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8164697/
https://www.ncbi.nlm.nih.gov/pubmed/34079304
http://dx.doi.org/10.2147/IDR.S299704

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